import pandas as pd
import numpy as np
import scanpy as sc
from sklearn.metrics.cluster import normalized_mutual_info_score, adjusted_rand_score
from sklearn.metrics import homogeneity_score, completeness_score, fowlkes_mallows_score, silhouette_score, davies_bouldin_score, calinski_harabasz_score
from sklearn.metrics.cluster import contingency_matrix, pair_confusion_matrix
from src.utils import sankey_plot
from sklearn.decomposition import PCA
import kaleido
from sklearn.preprocessing import StandardScaler
import plotly.io as pio
import matplotlib.pyplot as plt
import seaborn as snsClustering Comparision
Preamble
DIR = 'Data/'
DATASET_NAMES = ['PBMC1', 'PBMC2', 'PBMC3','PBMC4']
TOOLS = ['monocle', 'scanpy', 'scvi-tools', 'seurat', 'COTAN']
PARAMS_TUNING = ['default', 'celltypist', 'antibody']def compute_scores(dir, dataset, labels_df, labels_matched, ground_truth_labels):
scores = {}
scores['NMI'] = {}
scores['ARI'] = {}
scores['homogeneity'] = {}
scores['completeness'] = {}
scores['fowlkes_mallows'] = {}
scores['precision'] = {}
scores['recall'] = {}
for tool in TOOLS:
scores['NMI'][tool] = normalized_mutual_info_score(labels_pred=labels_df['cluster_'+tool], labels_true=labels_df[f'cluster_{ground_truth_labels}'], average_method='arithmetic')
scores['ARI'][tool] = adjusted_rand_score(labels_pred=labels_df['cluster_'+tool], labels_true=labels_df[f'cluster_{ground_truth_labels}'])
scores['homogeneity'][tool] = homogeneity_score(labels_pred=labels_df['cluster_'+tool], labels_true=labels_df[f'cluster_{ground_truth_labels}'])
scores['completeness'][tool] = completeness_score(labels_pred=labels_df['cluster_'+tool], labels_true=labels_df[f'cluster_{ground_truth_labels}'])
scores['fowlkes_mallows'][tool] = fowlkes_mallows_score(labels_pred=labels_df['cluster_'+tool], labels_true=labels_df[f'cluster_{ground_truth_labels}'])
sc = pair_confusion_matrix(labels_pred=labels_df['cluster_'+tool], labels_true=labels_df[f'cluster_{ground_truth_labels}'])
TP = sc[1,1]
FP = sc[0,1]
FN = sc[1,0]
P_score = TP/(TP+FP)
scores['precision'][tool] = P_score
scores['recall'][tool] = TP/(TP+FN)
scores_df = pd.DataFrame(scores)
scores_df.to_csv(f'{dir}{dataset}/scores_{labels_matched}_{ground_truth_labels}.csv')
scores_df.to_latex(f'{dir}{dataset}/scores_{labels_matched}_{ground_truth_labels}.tex')
display(scores_df)
def print_scores(dataset,tuning):
# concat tools labels
labels_df = pd.read_csv(f'{DIR}{dataset}/COTAN/{tuning}/clustering_labels.csv', index_col=0)
labels_df.rename(columns={"cluster": "cluster_COTAN"}, inplace=True)
#print("labels_df size")
#print(labels_df.shape)
for tool in [t for t in TOOLS if t != 'COTAN']:
tool_labels_df = pd.read_csv(f'{DIR}{dataset}/{tool}/{tuning}/clustering_labels.csv', index_col=0)
labels_df = labels_df.merge(tool_labels_df, how='inner', on='cell')
labels_df.rename(columns={"cluster": f"cluster_{tool}"}, inplace=True)
# print("labels_df size"+tool)
# print(labels_df.shape)
# load and concat celltypist labels
celltypist_df = pd.read_csv(f'{DIR}{dataset}/celltypist/celltypist_labels.csv', index_col=0)
celltypist_df.index = celltypist_df.index.str[:-2]
celltypist_df = labels_df.merge(celltypist_df, how='inner', on='cell')
celltypist_df.rename(columns={"cluster.ids": f"cluster_celltypist"}, inplace=True)
celltypist_mapping_df = pd.read_csv(f'{DIR}{dataset}/celltypist/celltypist_mapping.csv', index_col=0)
#print("celltypist_df size")
#print(celltypist_df.shape)
# load and concat protein surface labels
antibody_df = pd.read_csv(f'{DIR}{dataset}/antibody_annotation/antibody_labels_postproc.csv', index_col=0)
antibody_df = labels_df.merge(antibody_df, how='inner', on='cell')
antibody_df.rename(columns={"cluster.ids": f"cluster_antibody"}, inplace=True)
antibody_mapping_df = pd.read_csv(f'{DIR}{dataset}/antibody_annotation/antibody_mapping.csv', index_col=1, encoding='latin1')
#print("antibody_df size")
#print(antibody_df.shape)
# read dataset
adata = sc.read_10x_mtx(
f'{DIR}{dataset}/filtered/10X/',
var_names='gene_symbols',
cache=False
)
# keep only labelled cells
adata.var_names_make_unique()
subset_cells = adata.obs_names.isin(labels_df.index)
adata = adata[subset_cells, :]
mito_genes = adata.var_names.str.startswith('MT-')
# for each cell compute fraction of counts in mito genes vs. all genes
# the `.A1` is only necessary as X is sparse (to transform to a dense array after summing)
adata.obs['percent_mito'] = np.sum(adata[:, mito_genes].X, axis=1).A1 / np.sum(adata.X, axis=1).A1
# add the total counts per cell as observations-annotation to adata
adata.obs['n_counts'] = adata.X.sum(axis=1).A1
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, min_mean=0.00125, max_mean=3, min_disp=0.5)
adata.raw = adata
adata = adata[:, adata.var.highly_variable]
#sc.pp.regress_out(adata, ['n_counts', 'percent_mito'])
sc.pp.scale(adata, max_value=10)
sc.tl.pca(adata, svd_solver='arpack',n_comps=20)
pca_matrix = adata.obsm['X_pca']
scaler = StandardScaler()
scaled_pca_matrix = scaler.fit_transform(pca_matrix)
#Custers number
df = {}
for tool in TOOLS:
df[tool] = labels_df[f'cluster_{tool}'].unique().shape[0]
df_size = pd.DataFrame(df, index=[0])
display(f'{dataset} - number of clusters')
display(df_size)
# compute silhouette, Calinski_Harabasz and davies_bouldin scores with scaled PCA
silhouette = {}
Calinski_Harabasz = {}
davies_bouldin = {}
for tool in TOOLS:
silhouette[tool] = silhouette_score(scaled_pca_matrix, labels_df[f'cluster_{tool}'])
Calinski_Harabasz[tool] = calinski_harabasz_score(scaled_pca_matrix, labels_df[f'cluster_{tool}'])
davies_bouldin[tool] = davies_bouldin_score(scaled_pca_matrix, labels_df[f'cluster_{tool}'])
if tuning=='celltypist':
silhouette['celltypist'] = silhouette_score(scaled_pca_matrix, celltypist_df[f'cluster_celltypist'])
Calinski_Harabasz['celltypist'] = calinski_harabasz_score(scaled_pca_matrix, celltypist_df[f'cluster_celltypist'])
davies_bouldin['celltypist'] = davies_bouldin_score(scaled_pca_matrix, celltypist_df[f'cluster_celltypist'])
elif tuning=='antibody':
silhouette['antibody'] = silhouette_score(scaled_pca_matrix, antibody_df[f'cluster_antibody'])
Calinski_Harabasz['antibody'] = calinski_harabasz_score(scaled_pca_matrix, antibody_df[f'cluster_antibody'])
davies_bouldin['antibody'] = davies_bouldin_score(scaled_pca_matrix, antibody_df[f'cluster_antibody'])
silhouette_df = pd.DataFrame(silhouette, index=[0])
silhouette_df.to_csv(f'{DIR}{dataset}/{tuning}_silhouette.csv')
silhouette_df.to_latex(f'{DIR}{dataset}/{tuning}_silhouette.tex')
display(f'{dataset} - Silhuette (higher is better)')
display(silhouette_df)
Calinski_Harabasz_df = pd.DataFrame(Calinski_Harabasz, index=[0])
Calinski_Harabasz_df.to_csv(f'{DIR}{dataset}/{tuning}_Calinski_Harabasz.csv')
Calinski_Harabasz_df.to_latex(f'{DIR}{dataset}/{tuning}_Calinski_Harabasz.tex')
display(f'{dataset} - Calinski_Harabasz (higher is better)')
display(Calinski_Harabasz_df)
davies_bouldin_df = pd.DataFrame(davies_bouldin, index=[0])
davies_bouldin_df.to_csv(f'{DIR}{dataset}/{tuning}_davies_bouldin.csv')
davies_bouldin_df.to_latex(f'{DIR}{dataset}/{tuning}_davies_bouldin.tex')
display(f'{dataset} - davies_bouldin (lower is better)')
display(davies_bouldin_df)
# compute silhouette, Calinski_Harabasz and davies_bouldin scores with cellTypist probability
celltypist_prob_df = pd.read_csv(f'{DIR}{dataset}/celltypist/Immune_All_Low_probability_matrix.csv', index_col=0)
#labels_df = pd.read_csv(f'{DIR}{dataset}/COTAN/{tuning}/clustering_labels.csv', index_col=0)
celltypist_prob_df.index = celltypist_prob_df.index.str[:-2]
subset_cells = celltypist_prob_df.index.isin(labels_df.index)
celltypist_prob_df = celltypist_prob_df[subset_cells]
pca = PCA(n_components=20,svd_solver='arpack')
pca_data = pca.fit_transform(celltypist_prob_df)
df_prob = pd.DataFrame(pca_data)
df_prob.index = celltypist_prob_df.index
scaler = StandardScaler()
scaled_pca_data = pd.DataFrame(scaler.fit_transform(df_prob))
scaled_pca_data.index = celltypist_prob_df.index
silhouette = {}
Calinski_Harabasz = {}
davies_bouldin = {}
for tool in TOOLS:
silhouette[tool] = silhouette_score(scaled_pca_data, labels_df[f'cluster_{tool}'])
Calinski_Harabasz[tool] = calinski_harabasz_score(scaled_pca_data, labels_df[f'cluster_{tool}'])
davies_bouldin[tool] = davies_bouldin_score(scaled_pca_data, labels_df[f'cluster_{tool}'])
if tuning=='celltypist':
silhouette['celltypist'] = silhouette_score(scaled_pca_data, celltypist_df[f'cluster_celltypist'])
Calinski_Harabasz['celltypist'] = calinski_harabasz_score(scaled_pca_data, celltypist_df[f'cluster_celltypist'])
davies_bouldin['celltypist'] = davies_bouldin_score(scaled_pca_data, celltypist_df[f'cluster_celltypist'])
elif tuning=='antibody':
silhouette['antibody'] = silhouette_score(scaled_pca_data, antibody_df[f'cluster_antibody'])
Calinski_Harabasz['antibody'] = calinski_harabasz_score(scaled_pca_matrix, antibody_df[f'cluster_antibody'])
davies_bouldin['antibody'] = davies_bouldin_score(scaled_pca_matrix, antibody_df[f'cluster_antibody'])
silhouette_df = pd.DataFrame(silhouette, index=[0])
silhouette_df.to_csv(f'{DIR}{dataset}/{tuning}_silhouette_fromProb.csv')
silhouette_df.to_latex(f'{DIR}{dataset}/{tuning}_silhouette_fromProb.tex')
display(f'{dataset} - Silhuette from Prob. (higher is better)')
display(silhouette_df)
Calinski_Harabasz_df = pd.DataFrame(Calinski_Harabasz, index=[0])
Calinski_Harabasz_df.to_csv(f'{DIR}{dataset}/{tuning}_Calinski_Harabasz_fromProb.csv')
Calinski_Harabasz_df.to_latex(f'{DIR}{dataset}/{tuning}_Calinski_Harabasz_fromProb.tex')
display(f'{dataset} - Calinski_Harabasz from Prob. (higher is better)')
display(Calinski_Harabasz_df)
davies_bouldin_df = pd.DataFrame(davies_bouldin, index=[0])
davies_bouldin_df.to_csv(f'{DIR}{dataset}/{tuning}_davies_bouldin_fromProb.csv')
davies_bouldin_df.to_latex(f'{DIR}{dataset}/{tuning}_davies_bouldin_fromProb.tex')
display(f'{dataset} - davies_bouldin from Prob. (lower is better)')
display(davies_bouldin_df)
display(f'{dataset} - matching {tuning} labels' if tuning != 'default' else f'{dataset} - default labels')
# compute scores comparing each tool labels with celltypist labels
if tuning == 'celltypist' or tuning == 'default':
compute_scores(DIR, dataset, celltypist_df, tuning, 'celltypist')
labels = []
labels_titles = []
for tool in TOOLS:
labels.append(celltypist_df[f'cluster_{tool}'].to_list())
labels_titles.append(tool)
labels.append(celltypist_df[f'cluster_celltypist'].map(celltypist_mapping_df['go'].to_dict()).to_list())
labels_titles.append('celltypist')
title = f'{dataset} - matching {tuning} labels' if tuning != 'default' else f'{dataset} - default labels'
sankey_plot(labels=labels, labels_titles=labels_titles, title=title, path=f'{DIR}{dataset}/{tuning}_celltypist.html')
# compute scores comparing each tool labels with protein labels
if tuning == 'antibody' or tuning == 'default':
compute_scores(DIR, dataset, antibody_df, tuning, 'antibody')
labels = []
labels_titles = []
for tool in TOOLS:
labels.append(antibody_df[f'cluster_{tool}'].to_list())
labels_titles.append(tool)
labels.append(antibody_df[f'cluster_antibody'].map(antibody_mapping_df['go'].to_dict()).to_list())
labels_titles.append('antibody')
title = f'{dataset} - matching {tuning} labels' if tuning != 'default' else f'{dataset} - default labels'
sankey_plot(labels=labels, labels_titles=labels_titles, title=title, path=f'{DIR}{dataset}/{tuning}_antibody.html')def print_clustering_data(dataset,tuning):
# concat tools labels
labels_df = pd.read_csv(f'{DIR}{dataset}/COTAN/{tuning}/clustering_labels.csv', index_col=0)
labels_df.rename(columns={"cluster": "cluster_COTAN"}, inplace=True)
display(f'Initial COTAN cluster number:')
display(labels_df.cluster_COTAN.unique().shape[0])
#print("labels_df size")
#print(labels_df.shape)
for tool in [t for t in TOOLS if t != 'COTAN']:
tool_labels_df = pd.read_csv(f'{DIR}{dataset}/{tool}/{tuning}/clustering_labels.csv', index_col=0)
display(f'Initial {tool} cluster number:')
display(labels_df[labels_df.columns[-1]].unique().shape[0])
labels_df = labels_df.merge(tool_labels_df, how='inner', on='cell')
labels_df.rename(columns={"cluster": f"cluster_{tool}"}, inplace=True)
# print("labels_df size"+tool)
# print(labels_df.shape)
if tuning == 'celltypist' or tuning == 'default':
# load and concat celltypist labels
celltypist_df = pd.read_csv(f'{DIR}{dataset}/celltypist/celltypist_labels.csv', index_col=0)
celltypist_df.index = celltypist_df.index.str[:-2]
celltypist_df = labels_df.merge(celltypist_df, how='inner', on='cell')
celltypist_df.rename(columns={"cluster.ids": f"cluster_celltypist"}, inplace=True)
celltypist_mapping_df = pd.read_csv(f'{DIR}{dataset}/celltypist/celltypist_mapping.csv', index_col=0)
#print("celltypist_df size")
#print(celltypist_df.shape)
labels_cluster_celltypist = np.unique(celltypist_df["cluster_celltypist"])
for tool in TOOLS:
labels_cluster_tool = np.unique(celltypist_df[f'cluster_{tool}'])
cm =contingency_matrix(celltypist_df["cluster_celltypist"], celltypist_df[f'cluster_{tool}'])
cm = pd.DataFrame(cm,index=labels_cluster_celltypist,columns=labels_cluster_tool)
display(f'{dataset} - contingency_matrix (rows: cellTypist - cols: {tool})')
display(cm)
if tuning == 'antibody' or tuning == 'default':
#load and concat protein surface labels
antibody_df = pd.read_csv(f'{DIR}{dataset}/antibody_annotation/antibody_labels_postproc.csv', index_col=0)
display("Initial antibody cell/cluster table:")
display(antibody_df["cluster.ids"].value_counts())
antibody_df = labels_df.merge(antibody_df, how='inner', on='cell')
antibody_df.rename(columns={"cluster.ids": f"cluster_antibody"}, inplace=True)
antibody_mapping_df = pd.read_csv(f'{DIR}{dataset}/antibody_annotation/antibody_mapping.csv', index_col=1, encoding='latin1')
labels_cluster_antybody = np.unique(antibody_df["cluster_antibody"])
for tool in TOOLS:
labels_cluster_tool = np.unique(antibody_df[f'cluster_{tool}'])
cm =contingency_matrix(antibody_df["cluster_antibody"], antibody_df[f'cluster_{tool}'])
cm = pd.DataFrame(cm,index=labels_cluster_antybody,columns=labels_cluster_tool)
display(f'{dataset} - contingency_matrix (rows: antibody - cols: {tool})')
display(cm)
Data summary information
Default parameters
print_clustering_data(tuning = 'default',dataset="PBMC1")'Initial COTAN cluster number:'
14
'Initial monocle cluster number:'
14
'Initial scanpy cluster number:'
3
'Initial scvi-tools cluster number:'
18
'Initial seurat cluster number:'
13
'PBMC1 - contingency_matrix (rows: cellTypist - cols: monocle)'
| 1 | 2 | 3 | |
|---|---|---|---|
| 1 | 8 | 970 | 1 |
| 2 | 943 | 0 | 0 |
| 3 | 47 | 0 | 0 |
| 4 | 0 | 78 | 0 |
| 5 | 309 | 0 | 0 |
| 6 | 0 | 0 | 142 |
| 7 | 82 | 0 | 0 |
| 8 | 278 | 0 | 1 |
| 9 | 81 | 0 | 0 |
| 10 | 0 | 171 | 0 |
| 11 | 70 | 0 | 0 |
| 12 | 240 | 0 | 0 |
| 13 | 0 | 28 | 0 |
| 14 | 0 | 0 | 155 |
| 15 | 2 | 4 | 0 |
'PBMC1 - contingency_matrix (rows: cellTypist - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 246 | 0 | 267 | 0 | 263 | 0 | 200 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| 2 | 88 | 321 | 0 | 281 | 1 | 0 | 0 | 241 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 8 | 0 | 0 |
| 3 | 45 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 76 | 0 |
| 5 | 250 | 5 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 52 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 138 | 0 | 0 | 0 | 0 | 0 |
| 7 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 0 | 37 | 0 | 18 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 0 | 0 | 0 | 263 | 0 | 0 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 2 | 2 | 0 | 5 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 66 | 0 | 0 |
| 10 | 0 | 0 | 75 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 91 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 65 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 82 | 0 | 59 | 0 | 86 | 0 | 3 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 154 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 15 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: cellTypist - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 659 | 0 | 0 | 0 | 289 | 0 | 1 | 0 | 0 | 30 | 0 | 0 | 0 |
| 2 | 0 | 485 | 48 | 402 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 6 | 0 |
| 3 | 0 | 1 | 41 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 72 | 0 | 0 | 0 |
| 5 | 0 | 5 | 288 | 12 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 139 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 58 | 0 | 0 | 23 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 0 | 0 | 0 | 0 | 279 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 4 | 0 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 73 | 0 |
| 10 | 1 | 0 | 0 | 0 | 78 | 0 | 0 | 0 | 0 | 0 | 92 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 67 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 1 | 48 | 1 | 0 | 189 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
| 14 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 147 | 7 | 0 | 0 | 0 | 0 |
| 15 | 0 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: cellTypist - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 616 | 361 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 2 | 798 | 145 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1 | 46 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 74 | 0 |
| 5 | 0 | 309 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 142 | 0 | 0 | 0 |
| 7 | 0 | 55 | 0 | 0 | 0 | 27 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 0 | 0 | 274 | 5 | 0 | 0 | 0 | 0 | 0 |
| 9 | 78 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 43 | 0 | 0 | 0 | 0 | 128 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 5 | 65 | 0 | 0 | 0 | 0 | 0 |
| 12 | 7 | 69 | 0 | 0 | 0 | 164 | 0 | 0 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 27 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 153 | 2 | 0 | 0 | 0 |
| 15 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: cellTypist - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 35 | 0 | 11 | 648 | 284 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 807 | 129 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37 | 2 | 8 |
| 4 | 0 | 73 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 308 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 142 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 1 | 0 | 0 | 56 |
| 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 55 | 4 | 220 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 34 | 1 | 0 | 0 | 45 | 1 |
| 10 | 0 | 1 | 146 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 67 | 1 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 175 | 0 | 1 | 5 | 56 |
| 13 | 0 | 1 | 0 | 0 | 0 | 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 152 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 |
'Initial antibody cell/cluster table:'
cluster.ids
7 1338
8 876
3 748
4 341
9 331
5 211
1 202
6 131
2 62
10 51
12 16
Name: count, dtype: int64
'PBMC1 - contingency_matrix (rows: antibody - cols: monocle)'
| 1 | 2 | 3 | |
|---|---|---|---|
| 1 | 161 | 0 | 0 |
| 2 | 0 | 43 | 3 |
| 3 | 600 | 0 | 0 |
| 4 | 262 | 1 | 0 |
| 5 | 158 | 0 | 0 |
| 6 | 1 | 86 | 0 |
| 7 | 10 | 1115 | 0 |
| 8 | 812 | 1 | 1 |
| 9 | 1 | 0 | 294 |
| 10 | 44 | 0 | 0 |
| 12 | 10 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 7 | 6 | 0 | 4 | 0 | 0 | 0 | 9 | 0 | 23 | 0 | 7 | 0 | 32 | 0 | 73 | 0 | 0 |
| 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 0 | 38 | 0 | 0 | 0 |
| 3 | 366 | 33 | 0 | 5 | 0 | 0 | 0 | 28 | 0 | 43 | 0 | 122 | 0 | 3 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 1 | 249 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 64 | 0 | 15 | 0 | 68 | 0 | 0 | 0 | 0 |
| 6 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 56 | 26 |
| 7 | 1 | 0 | 319 | 0 | 267 | 0 | 267 | 1 | 198 | 0 | 0 | 0 | 0 | 0 | 52 | 0 | 19 | 1 |
| 8 | 18 | 288 | 0 | 277 | 0 | 1 | 0 | 214 | 0 | 3 | 1 | 5 | 0 | 2 | 0 | 4 | 0 | 1 |
| 9 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 156 | 0 | 137 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 36 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 6 | 12 | 5 | 0 | 62 | 0 | 0 | 0 | 0 | 0 | 76 | 0 |
| 2 | 2 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 1 | 0 | 39 | 0 | 0 |
| 3 | 0 | 49 | 441 | 66 | 0 | 44 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1 | 0 | 0 | 0 | 0 | 0 | 262 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 0 | 2 | 12 | 1 | 0 | 141 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| 6 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 56 | 1 | 0 | 26 |
| 7 | 655 | 3 | 0 | 0 | 368 | 0 | 1 | 0 | 0 | 45 | 52 | 0 | 1 |
| 8 | 0 | 438 | 17 | 349 | 0 | 4 | 3 | 0 | 0 | 0 | 0 | 2 | 1 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 148 | 144 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 1 | 0 | 0 | 37 | 6 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 93 | 17 | 0 | 0 | 0 | 51 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 1 | 4 | 0 | 0 | 1 | 1 | 39 | 0 | 0 |
| 3 | 23 | 540 | 0 | 0 | 0 | 37 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 1 | 0 | 260 | 2 | 0 | 0 | 0 | 0 | 0 |
| 5 | 1 | 28 | 0 | 0 | 0 | 129 | 0 | 0 | 0 | 0 | 0 |
| 6 | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 56 | 25 |
| 7 | 2 | 1 | 611 | 402 | 1 | 0 | 0 | 0 | 89 | 18 | 1 |
| 8 | 766 | 41 | 0 | 0 | 1 | 4 | 1 | 0 | 0 | 0 | 1 |
| 9 | 0 | 0 | 0 | 0 | 2 | 0 | 151 | 142 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 8 | 36 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 8 | 2 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 54 | 0 | 1 | 59 | 11 |
| 2 | 1 | 1 | 40 | 2 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 0 | 38 | 33 | 490 |
| 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 1 | 213 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 135 | 0 | 0 | 0 | 22 |
| 6 | 0 | 56 | 1 | 1 | 1 | 25 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| 7 | 33 | 17 | 118 | 668 | 284 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 |
| 8 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 9 | 4 | 0 | 0 | 765 | 34 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 150 | 143 | 0 | 0 | 2 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 38 | 2 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 5 | 0 | 0 | 0 |
print_clustering_data(tuning = 'default',dataset="PBMC2")'Initial COTAN cluster number:'
17
'Initial monocle cluster number:'
17
'Initial scanpy cluster number:'
2
'Initial scvi-tools cluster number:'
18
'Initial seurat cluster number:'
20
'PBMC2 - contingency_matrix (rows: cellTypist - cols: monocle)'
| 1 | 2 | |
|---|---|---|
| 1 | 230 | 1 |
| 2 | 427 | 0 |
| 3 | 2139 | 3 |
| 4 | 700 | 7 |
| 5 | 316 | 0 |
| 6 | 0 | 93 |
| 7 | 0 | 567 |
| 8 | 674 | 0 |
| 9 | 0 | 186 |
| 10 | 52 | 0 |
| 11 | 0 | 228 |
| 12 | 0 | 204 |
| 13 | 0 | 48 |
| 14 | 0 | 14 |
| 15 | 80 | 0 |
| 16 | 0 | 6 |
'PBMC2 - contingency_matrix (rows: cellTypist - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 22 | 1 | 2 | 5 | 50 | 0 | 148 | 0 | 0 | 0 | 0 |
| 2 | 0 | 91 | 0 | 273 | 0 | 0 | 0 | 3 | 2 | 0 | 56 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| 3 | 942 | 508 | 21 | 183 | 0 | 0 | 0 | 21 | 295 | 2 | 100 | 42 | 0 | 26 | 1 | 0 | 0 | 1 |
| 4 | 0 | 0 | 0 | 1 | 0 | 463 | 8 | 2 | 0 | 230 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 266 | 0 | 0 | 42 | 0 | 0 | 6 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88 | 0 | 0 |
| 7 | 0 | 0 | 0 | 0 | 466 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 0 | 86 | 0 | 0 | 0 |
| 8 | 2 | 1 | 558 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 110 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 174 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 228 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 204 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 |
| 15 | 0 | 25 | 0 | 46 | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC2 - contingency_matrix (rows: cellTypist - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 222 | 1 | 2 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 331 | 4 | 1 | 0 | 0 | 0 | 7 | 10 | 53 | 0 | 0 | 1 | 0 | 10 | 4 | 0 | 6 | 0 | 0 | 0 |
| 3 | 391 | 733 | 2 | 1 | 19 | 1 | 41 | 304 | 158 | 185 | 0 | 2 | 43 | 90 | 75 | 0 | 71 | 0 | 0 | 26 |
| 4 | 1 | 0 | 675 | 1 | 0 | 8 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 0 | 0 |
| 5 | 7 | 0 | 1 | 0 | 0 | 0 | 11 | 9 | 66 | 0 | 0 | 151 | 71 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 78 | 0 | 0 | 3 | 0 |
| 7 | 0 | 0 | 0 | 564 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 2 | 0 | 1 | 561 | 0 | 103 | 1 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 175 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10 | 2 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 44 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 228 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 204 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 58 | 0 | 1 | 0 | 0 | 0 | 5 | 5 | 1 | 5 | 0 | 0 | 1 | 3 | 0 | 0 | 1 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC2 - contingency_matrix (rows: cellTypist - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 4 | 6 | 0 | 0 | 0 | 219 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| 2 | 0 | 400 | 23 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1151 | 352 | 550 | 0 | 2 | 15 | 69 | 1 | 0 | 0 | 0 | 2 | 0 | 0 |
| 4 | 0 | 0 | 2 | 635 | 0 | 0 | 3 | 7 | 0 | 0 | 1 | 59 | 0 | 0 |
| 5 | 0 | 42 | 0 | 0 | 0 | 0 | 7 | 0 | 267 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 82 | 0 |
| 7 | 0 | 0 | 0 | 0 | 567 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 8 | 4 | 2 | 0 | 0 | 541 | 119 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 172 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 83 | 0 | 0 | 145 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 201 | 1 | 0 | 2 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 |
| 15 | 0 | 0 | 77 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 |
'PBMC2 - contingency_matrix (rows: cellTypist - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 3 | 4 | 220 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 416 | 9 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 2 | 1186 | 847 | 31 | 72 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 4 | 639 | 56 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 1 |
| 5 | 0 | 0 | 0 | 39 | 0 | 7 | 270 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 86 | 0 | 7 | 0 | 0 | 0 | 0 | 0 |
| 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88 | 300 | 174 | 0 | 0 | 3 | 1 |
| 8 | 0 | 0 | 568 | 9 | 1 | 96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 154 | 24 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 |
| 10 | 1 | 51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 145 | 69 | 14 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 22 | 180 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 0 | 0 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 1 |
| 15 | 0 | 1 | 0 | 6 | 72 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 |
'Initial antibody cell/cluster table:'
cluster.ids
4 1510
11 1130
8 695
12 570
6 424
13 275
5 197
2 150
10 122
3 84
7 76
Name: count, dtype: int64
'PBMC2 - contingency_matrix (rows: antibody - cols: monocle)'
| 1 | 2 | |
|---|---|---|
| 2 | 0 | 147 |
| 3 | 60 | 20 |
| 4 | 1480 | 13 |
| 5 | 196 | 0 |
| 6 | 416 | 1 |
| 7 | 68 | 7 |
| 8 | 681 | 5 |
| 10 | 0 | 119 |
| 11 | 1115 | 9 |
| 12 | 566 | 4 |
| 13 | 0 | 271 |
'PBMC2 - contingency_matrix (rows: antibody - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 141 | 0 | 2 | 0 | 0 | 0 |
| 3 | 0 | 1 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 54 | 0 | 0 | 18 | 2 | 1 | 0 | 0 | 0 |
| 4 | 89 | 588 | 0 | 478 | 0 | 0 | 0 | 56 | 63 | 0 | 194 | 10 | 1 | 3 | 3 | 4 | 3 | 1 |
| 5 | 7 | 1 | 14 | 2 | 0 | 0 | 0 | 118 | 2 | 2 | 4 | 23 | 0 | 23 | 0 | 0 | 0 | 0 |
| 6 | 0 | 2 | 9 | 13 | 0 | 0 | 1 | 128 | 1 | 0 | 3 | 124 | 0 | 136 | 0 | 0 | 0 | 0 |
| 7 | 1 | 3 | 27 | 3 | 0 | 0 | 0 | 6 | 1 | 0 | 1 | 23 | 0 | 3 | 0 | 5 | 2 | 0 |
| 8 | 0 | 0 | 0 | 0 | 0 | 459 | 4 | 0 | 0 | 220 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 |
| 10 | 0 | 0 | 0 | 0 | 107 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 6 | 0 | 0 |
| 11 | 843 | 26 | 5 | 5 | 0 | 0 | 5 | 4 | 229 | 0 | 1 | 2 | 0 | 0 | 1 | 2 | 1 | 0 |
| 12 | 2 | 0 | 522 | 0 | 0 | 0 | 2 | 1 | 1 | 2 | 0 | 21 | 0 | 17 | 1 | 0 | 1 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 263 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 1 | 0 |
'PBMC2 - contingency_matrix (rows: antibody - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 142 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 55 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 |
| 4 | 758 | 143 | 0 | 4 | 0 | 0 | 30 | 125 | 259 | 52 | 0 | 5 | 4 | 54 | 26 | 4 | 23 | 0 | 3 | 3 |
| 5 | 4 | 8 | 1 | 0 | 5 | 0 | 27 | 4 | 6 | 0 | 0 | 36 | 105 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 4 | 0 | 0 | 0 | 17 | 0 | 270 | 2 | 9 | 0 | 0 | 112 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 1 | 0 | 1 | 31 | 0 | 27 | 2 | 1 | 1 | 0 | 5 | 0 | 0 | 0 | 4 | 0 | 0 | 2 | 0 |
| 8 | 0 | 0 | 622 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 56 | 1 | 0 |
| 10 | 0 | 0 | 0 | 111 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 |
| 11 | 14 | 581 | 0 | 1 | 5 | 5 | 2 | 191 | 5 | 138 | 0 | 0 | 1 | 50 | 51 | 2 | 54 | 0 | 1 | 23 |
| 12 | 0 | 2 | 1 | 1 | 520 | 2 | 34 | 1 | 0 | 1 | 0 | 0 | 6 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 13 | 0 | 0 | 0 | 7 | 0 | 263 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
'PBMC2 - contingency_matrix (rows: antibody - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 139 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 1 | 9 | 2 | 1 | 1 | 0 | 0 | 18 | 0 | 48 | 0 | 0 |
| 4 | 65 | 756 | 607 | 0 | 4 | 0 | 10 | 0 | 42 | 0 | 0 | 2 | 4 | 3 |
| 5 | 6 | 6 | 2 | 0 | 0 | 12 | 65 | 0 | 104 | 0 | 0 | 1 | 0 | 0 |
| 6 | 0 | 22 | 3 | 0 | 0 | 7 | 267 | 0 | 118 | 0 | 0 | 0 | 0 | 0 |
| 7 | 2 | 7 | 5 | 0 | 0 | 21 | 30 | 0 | 3 | 0 | 0 | 0 | 5 | 2 |
| 8 | 0 | 0 | 0 | 627 | 0 | 0 | 0 | 3 | 0 | 2 | 1 | 52 | 0 | 1 |
| 10 | 0 | 0 | 0 | 0 | 113 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 3 | 0 |
| 11 | 1073 | 4 | 35 | 0 | 1 | 1 | 1 | 4 | 1 | 0 | 1 | 0 | 2 | 1 |
| 12 | 9 | 0 | 0 | 0 | 1 | 512 | 44 | 1 | 0 | 0 | 1 | 1 | 0 | 1 |
| 13 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 159 | 1 | 0 | 103 | 0 | 0 | 1 |
'PBMC2 - contingency_matrix (rows: antibody - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 137 | 4 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 |
| 3 | 8 | 49 | 1 | 2 | 0 | 1 | 0 | 1 | 17 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 76 | 1235 | 110 | 16 | 44 | 0 | 0 | 4 | 3 | 0 | 1 | 3 | 0 | 1 | 0 |
| 5 | 0 | 0 | 19 | 6 | 1 | 65 | 105 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 11 | 23 | 1 | 261 | 120 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 32 | 16 | 1 | 15 | 4 | 0 | 0 | 5 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
| 8 | 630 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 5 | 1 | 76 | 33 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 1082 | 27 | 4 | 1 | 1 | 0 | 0 | 2 | 1 | 0 | 0 | 1 | 1 | 3 | 1 |
| 12 | 0 | 1 | 526 | 0 | 0 | 38 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 | 0 | 0 | 1 | 102 | 29 | 133 |
print_clustering_data(tuning = 'default',dataset="PBMC3")'Initial COTAN cluster number:'
32
'Initial monocle cluster number:'
32
'Initial scanpy cluster number:'
3
'Initial scvi-tools cluster number:'
22
'Initial seurat cluster number:'
17
'PBMC3 - contingency_matrix (rows: cellTypist - cols: monocle)'
| 1 | 2 | 3 | |
|---|---|---|---|
| 1 | 3021 | 0 | 0 |
| 2 | 1 | 1471 | 0 |
| 3 | 6 | 1 | 655 |
| 4 | 1100 | 0 | 0 |
| 5 | 1183 | 26 | 33 |
| 6 | 0 | 156 | 0 |
| 7 | 1112 | 1 | 0 |
| 8 | 484 | 0 | 0 |
| 9 | 0 | 0 | 396 |
| 10 | 0 | 408 | 0 |
| 11 | 430 | 0 | 0 |
| 12 | 0 | 0 | 8 |
| 13 | 233 | 0 | 0 |
| 14 | 111 | 0 | 0 |
| 15 | 4 | 16 | 0 |
| 16 | 0 | 11 | 0 |
| 17 | 0 | 8 | 0 |
| 18 | 0 | 57 | 0 |
| 19 | 0 | 12 | 0 |
'PBMC3 - contingency_matrix (rows: cellTypist - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1401 | 0 | 153 | 0 | 0 | 29 | 26 | 534 | 121 | 429 | ... | 1 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 28 | 0 |
| 2 | 0 | 0 | 0 | 0 | 816 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 2 | 0 | 227 | 0 | 5 | 0 | 35 | 0 | 0 |
| 3 | 0 | 0 | 0 | 543 | 0 | 0 | 0 | 0 | 5 | 0 | ... | 0 | 0 | 0 | 0 | 111 | 0 | 0 | 2 | 0 | 0 |
| 4 | 26 | 0 | 0 | 0 | 0 | 806 | 12 | 5 | 29 | 6 | ... | 0 | 0 | 155 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 5 | 0 | 961 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | ... | 216 | 0 | 0 | 29 | 33 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 147 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 683 | 0 | 0 | 0 | 7 | 128 | 236 | 15 | ... | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 0 | 0 | 0 | 0 | 423 | 0 | 32 | 0 | ... | 4 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 395 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 90 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 311 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
| 11 | 0 | 2 | 0 | 0 | 0 | 0 | 281 | 2 | 54 | 0 | ... | 4 | 0 | 42 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 |
| 13 | 12 | 0 | 116 | 0 | 0 | 0 | 1 | 52 | 9 | 11 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 5 | 1 | 0 | 0 | 0 | 5 | 0 | 3 | 0 | ... | 97 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 |
| 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 0 | 0 | 0 |
| 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 |
19 rows × 22 columns
'PBMC3 - contingency_matrix (rows: cellTypist - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 740 | 1603 | 2 | 1 | 1 | 40 | 86 | 23 | 248 | 1 | 197 | 79 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 1460 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 8 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 4 | 2 | 655 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 6 | 11 | 0 | 1 | 1 | 912 | 162 | 2 | 1 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 0 | 0 | 29 | 1165 | 31 | 0 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 1 | 0 |
| 6 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 146 | 0 | 0 | 0 | 0 |
| 7 | 883 | 14 | 1 | 0 | 0 | 1 | 25 | 29 | 80 | 0 | 70 | 10 | 0 | 0 | 0 | 0 | 0 |
| 8 | 24 | 0 | 0 | 6 | 0 | 1 | 5 | 313 | 0 | 0 | 0 | 135 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 396 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 97 | 0 | 0 | 0 | 0 | 0 | 0 | 311 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 10 | 0 | 0 | 2 | 0 | 2 | 408 | 2 | 0 | 0 | 1 | 4 | 0 | 1 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 13 | 174 | 26 | 0 | 0 | 0 | 0 | 3 | 0 | 25 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 0 | 1 | 19 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
| 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 |
| 18 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 55 | 0 | 0 |
| 19 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC3 - contingency_matrix (rows: cellTypist - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1740 | 415 | 0 | 0 | 681 | 20 | 162 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 1013 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 227 | 0 | 229 | 3 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 535 | 0 | 2 | 0 | 1 | 2 | 0 | 0 | 120 | 0 | 0 |
| 4 | 16 | 6 | 0 | 0 | 3 | 886 | 188 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 5 | 0 | 1 | 4 | 1043 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 24 | 136 | 0 | 0 | 30 | 0 | 0 |
| 6 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 149 | 0 | 0 | 0 |
| 7 | 0 | 980 | 0 | 0 | 108 | 0 | 18 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 18 | 0 | 3 | 0 | 0 | 8 | 0 | 454 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 0 | 336 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 |
| 10 | 0 | 0 | 84 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 319 | 1 | 0 | 4 | 0 | 0 | 0 | 0 |
| 11 | 0 | 14 | 0 | 0 | 0 | 2 | 411 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 13 | 13 | 5 | 0 | 0 | 208 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 109 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
| 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 |
| 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 52 | 0 |
| 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
'PBMC3 - contingency_matrix (rows: cellTypist - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 48 | 6 | 205 | 1790 | 15 | 4 | 53 | 199 | 154 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | ... | 67 | 125 | 220 | 131 | 0 | 0 | 2 | 0 | 0 | 0 |
| 4 | 0 | 152 | 1 | 89 | 18 | 805 | 0 | 2 | 7 | 20 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | ... | 3 | 0 | 3 | 0 | 30 | 4 | 138 | 1025 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 12 | 9 | 26 | 0 | 0 | 4 | 431 | 546 | 27 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 8 | 0 | 4 | 465 | 0 | 0 | 0 | 0 | 1 | 8 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 30 | 15 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 83 | 272 | 2 | 9 | 0 | 1 | 0 | 0 | 7 | 8 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 1 | 32 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 |
| 13 | 0 | 0 | 0 | 4 | 5 | 0 | 139 | 3 | 1 | 6 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 |
| 14 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 86 | 17 | 6 | 0 | 0 |
| 15 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 |
| 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 52 |
| 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 11 | 0 |
19 rows × 32 columns
'Initial antibody cell/cluster table:'
cluster.ids
9 2220
10 1635
7 1271
13 1067
5 1010
12 909
6 744
2 271
4 214
14 168
3 149
23 133
22 71
Name: count, dtype: int64
'PBMC3 - contingency_matrix (rows: antibody - cols: monocle)'
| 1 | 2 | 3 | |
|---|---|---|---|
| 2 | 3 | 265 | 0 |
| 3 | 132 | 13 | 3 |
| 4 | 209 | 2 | 1 |
| 5 | 993 | 14 | 1 |
| 6 | 741 | 0 | 0 |
| 7 | 4 | 1226 | 0 |
| 9 | 2201 | 9 | 1 |
| 10 | 1616 | 12 | 3 |
| 12 | 905 | 2 | 1 |
| 13 | 2 | 43 | 1002 |
| 14 | 141 | 23 | 1 |
| 22 | 70 | 1 | 0 |
| 23 | 130 | 0 | 0 |
'PBMC3 - contingency_matrix (rows: antibody - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 0 | 1 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 223 | 0 | 25 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3 | 0 | 14 | 2 | 0 | 0 | 1 | 5 | 0 | 3 | 0 | ... | 106 | 12 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 |
| 4 | 4 | 0 | 5 | 0 | 0 | 50 | 25 | 10 | 28 | 15 | ... | 1 | 0 | 57 | 0 | 1 | 2 | 0 | 0 | 1 | 0 |
| 5 | 0 | 912 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | ... | 80 | 10 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 |
| 6 | 1 | 0 | 8 | 0 | 0 | 9 | 433 | 11 | 75 | 1 | ... | 3 | 0 | 151 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 1 | 0 | 735 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 20 | 0 | 167 | 0 | 28 | 2 | 0 | 0 | 1 |
| 9 | 92 | 0 | 808 | 1 | 0 | 1 | 26 | 593 | 323 | 196 | ... | 0 | 3 | 19 | 1 | 0 | 5 | 0 | 0 | 7 | 0 |
| 10 | 1252 | 2 | 4 | 0 | 0 | 15 | 3 | 18 | 4 | 205 | ... | 3 | 1 | 5 | 3 | 6 | 4 | 2 | 0 | 20 | 2 |
| 12 | 25 | 1 | 0 | 0 | 1 | 737 | 34 | 0 | 8 | 2 | ... | 6 | 0 | 40 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 873 | 1 | 0 | 1 | 0 | 1 | 0 | ... | 0 | 6 | 0 | 1 | 128 | 2 | 2 | 30 | 0 | 0 |
| 14 | 0 | 17 | 0 | 0 | 0 | 0 | 4 | 0 | 2 | 1 | ... | 116 | 20 | 0 | 1 | 1 | 0 | 3 | 0 | 0 | 0 |
| 22 | 10 | 0 | 9 | 0 | 0 | 0 | 2 | 18 | 6 | 19 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 23 | 0 | 0 | 90 | 0 | 0 | 0 | 0 | 18 | 1 | 2 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
13 rows × 22 columns
'PBMC3 - contingency_matrix (rows: antibody - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 0 | 0 | 42 | 2 | 0 | 0 | 0 | 0 | 0 | 223 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 3 | 3 | 0 | 1 | 48 | 2 | 1 | 6 | 1 | 0 | 12 | 0 | 0 | 0 | 74 | 0 | 0 | 0 |
| 4 | 18 | 8 | 1 | 0 | 2 | 76 | 80 | 18 | 0 | 0 | 7 | 0 | 2 | 0 | 0 | 0 | 0 |
| 5 | 0 | 0 | 1 | 979 | 1 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 14 | 0 | 2 | 1 |
| 6 | 14 | 1 | 1 | 1 | 0 | 23 | 464 | 222 | 0 | 0 | 6 | 6 | 0 | 3 | 0 | 0 | 0 |
| 7 | 2 | 0 | 1182 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 0 | 0 | 26 | 0 | 1 | 1 | 0 |
| 9 | 1524 | 275 | 1 | 0 | 2 | 1 | 26 | 47 | 171 | 3 | 134 | 22 | 5 | 0 | 0 | 0 | 0 |
| 10 | 52 | 1281 | 1 | 6 | 4 | 15 | 4 | 2 | 149 | 2 | 101 | 4 | 6 | 0 | 2 | 2 | 0 |
| 12 | 0 | 5 | 2 | 4 | 1 | 816 | 67 | 5 | 2 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 |
| 13 | 0 | 0 | 31 | 1 | 995 | 0 | 1 | 1 | 8 | 7 | 0 | 0 | 2 | 0 | 1 | 0 | 0 |
| 14 | 0 | 0 | 1 | 129 | 1 | 0 | 0 | 3 | 0 | 20 | 0 | 1 | 0 | 7 | 3 | 0 | 0 |
| 22 | 39 | 14 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 0 | 6 | 3 | 0 | 0 | 1 | 0 | 0 |
| 23 | 111 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC3 - contingency_matrix (rows: antibody - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 0 | 0 | 18 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 226 | 20 | 1 | 0 | 0 | 0 | 1 | 0 |
| 3 | 0 | 3 | 0 | 25 | 0 | 1 | 1 | 0 | 2 | 0 | 12 | 0 | 101 | 0 | 0 | 2 | 0 | 1 |
| 4 | 11 | 28 | 0 | 0 | 12 | 48 | 97 | 0 | 12 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 |
| 5 | 0 | 0 | 0 | 980 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 1 | 14 | 0 | 0 | 1 | 0 | 2 |
| 6 | 1 | 22 | 0 | 0 | 6 | 16 | 468 | 0 | 225 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 2 | 873 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 139 | 0 | 164 | 27 | 0 | 2 | 1 |
| 9 | 129 | 1302 | 0 | 0 | 716 | 0 | 31 | 1 | 23 | 0 | 3 | 1 | 0 | 0 | 5 | 0 | 0 | 0 |
| 10 | 1543 | 8 | 1 | 2 | 46 | 3 | 6 | 0 | 2 | 0 | 0 | 3 | 3 | 0 | 4 | 4 | 2 | 4 |
| 12 | 6 | 0 | 1 | 0 | 3 | 816 | 72 | 0 | 4 | 0 | 0 | 1 | 4 | 0 | 0 | 1 | 0 | 0 |
| 13 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 549 | 1 | 327 | 7 | 30 | 0 | 1 | 2 | 126 | 2 | 0 |
| 14 | 0 | 1 | 0 | 14 | 1 | 0 | 0 | 0 | 3 | 0 | 20 | 1 | 121 | 0 | 0 | 1 | 3 | 0 |
| 22 | 12 | 19 | 0 | 0 | 34 | 0 | 3 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 23 | 0 | 4 | 0 | 0 | 126 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC3 - contingency_matrix (rows: antibody - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 14 | 0 | 1 | 2 | 0 | 1 |
| 3 | 1 | 0 | 1 | 3 | 0 | 1 | 0 | 0 | 1 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 72 | 28 | 25 | 1 | 0 |
| 4 | 3 | 29 | 15 | 13 | 15 | 50 | 2 | 9 | 15 | 44 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 |
| 5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 12 | 11 | 2 | 968 | 2 | 0 |
| 6 | 62 | 357 | 227 | 8 | 1 | 13 | 1 | 14 | 11 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 15 | 0 | 18 | 0 |
| 7 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 2 |
| 9 | 1 | 21 | 28 | 159 | 158 | 0 | 30 | 440 | 716 | 134 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 8 | 0 | 2 | 0 |
| 10 | 0 | 5 | 3 | 3 | 1550 | 3 | 0 | 6 | 2 | 5 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 2 | 2 |
| 12 | 6 | 47 | 4 | 79 | 5 | 739 | 0 | 0 | 1 | 3 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 6 | 0 |
| 13 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | ... | 65 | 116 | 233 | 128 | 1 | 1 | 0 | 0 | 8 | 2 |
| 14 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 4 | 116 | 14 | 0 | 3 |
| 22 | 0 | 0 | 2 | 2 | 19 | 0 | 1 | 8 | 6 | 9 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 23 | 0 | 0 | 0 | 4 | 0 | 0 | 109 | 4 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
13 rows × 32 columns
print_clustering_data(tuning = 'default',dataset="PBMC4")'Initial COTAN cluster number:'
24
'Initial monocle cluster number:'
24
'Initial scanpy cluster number:'
3
'Initial scvi-tools cluster number:'
22
'Initial seurat cluster number:'
16
'PBMC4 - contingency_matrix (rows: cellTypist - cols: monocle)'
| 1 | 2 | 3 | |
|---|---|---|---|
| 1 | 407 | 0 | 0 |
| 2 | 11 | 0 | 797 |
| 3 | 1330 | 1 | 0 |
| 4 | 108 | 0 | 0 |
| 5 | 9 | 2178 | 13 |
| 6 | 308 | 0 | 0 |
| 7 | 77 | 0 | 0 |
| 8 | 538 | 0 | 0 |
| 9 | 358 | 1 | 0 |
| 10 | 0 | 307 | 0 |
| 11 | 1 | 1 | 222 |
| 12 | 0 | 28 | 0 |
| 13 | 8 | 3 | 2 |
| 14 | 106 | 0 | 0 |
| 15 | 0 | 92 | 1 |
| 16 | 0 | 59 | 0 |
'PBMC4 - contingency_matrix (rows: cellTypist - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 384 | 1 | 0 | ... | 0 | 6 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 0 |
| 2 | 0 | 673 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 34 |
| 3 | 300 | 0 | 0 | 0 | 496 | 385 | 0 | 1 | 0 | 0 | ... | 136 | 10 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 10 | 0 | ... | 0 | 7 | 0 | 0 | 0 | 0 | 85 | 0 | 0 | 0 |
| 5 | 1 | 0 | 596 | 456 | 0 | 0 | 427 | 0 | 0 | 281 | ... | 0 | 0 | 0 | 169 | 76 | 145 | 0 | 48 | 0 | 0 |
| 6 | 7 | 0 | 0 | 0 | 4 | 3 | 0 | 0 | 0 | 0 | ... | 8 | 99 | 187 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 67 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 |
| 8 | 462 | 0 | 0 | 0 | 8 | 46 | 0 | 1 | 0 | 0 | ... | 19 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 348 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 74 | 0 | 0 | 1 | 0 | 0 | 2 | ... | 0 | 0 | 0 | 10 | 2 | 1 | 0 | 1 | 0 | 0 |
| 11 | 0 | 46 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 0 | 0 |
| 13 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 42 | 0 | 0 | 0 | 1 | 13 | 0 | 1 | 0 | 0 | ... | 47 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 2 | 88 | 2 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 0 |
16 rows × 22 columns
'PBMC4 - contingency_matrix (rows: cellTypist - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 4 | 1 | 0 | 0 | 1 | 0 | 315 | 0 | 0 | 0 | 86 | 0 | 0 | 0 |
| 2 | 800 | 0 | 2 | 1 | 0 | 0 | 2 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| 3 | 0 | 939 | 357 | 2 | 0 | 0 | 20 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 105 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 5 | 4 | 0 | 1 | 805 | 746 | 413 | 0 | 4 | 2 | 1 | 167 | 15 | 1 | 41 | 0 | 0 |
| 6 | 0 | 8 | 8 | 0 | 0 | 0 | 291 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 3 | 0 | 0 | 0 | 67 | 1 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 31 | 483 | 0 | 0 | 0 | 3 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 15 |
| 9 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 355 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 14 | 0 | 94 | 1 | 0 | 0 | 194 | 4 | 0 | 0 | 0 | 0 | 0 |
| 11 | 217 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 0 | 0 |
| 13 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 |
| 14 | 1 | 9 | 89 | 0 | 0 | 0 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 15 | 0 | 0 | 0 | 2 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 87 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 58 | 0 |
'PBMC4 - contingency_matrix (rows: cellTypist - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 5 | 0 | 0 | 0 | 0 | 0 | 398 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 632 | 0 | 1 | 0 | 122 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 52 | 0 |
| 3 | 356 | 854 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 116 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 107 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 2 | 0 | 780 | 723 | 0 | 447 | 2 | 0 | 2 | 0 | 1 | 1 | 0 | 136 | 28 | 35 | 0 | 0 | 43 |
| 6 | 31 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 68 | 5 | 0 | 189 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 69 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 525 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 354 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 7 | 0 | 86 | 0 | 0 | 0 | 0 | 0 | 213 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 223 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 0 | 0 | 0 |
| 13 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 |
| 14 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 101 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 0 | 0 | 1 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 0 | 0 |
'PBMC4 - contingency_matrix (rows: cellTypist - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 402 | 0 | 0 | 4 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 45 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 45 | 963 | 322 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 105 | 0 | 0 | 0 | 0 | 3 |
| 5 | 40 | 131 | 724 | 199 | 332 | 741 | 1 | 27 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 209 | 95 |
| 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 3 | 0 | 4 | 2 | 0 | 68 |
| 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 | 530 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 55 | 36 | 118 | 147 | 1 | 1 | 0 | 1 |
| 10 | 0 | 1 | 6 | 0 | 83 | 2 | 215 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | ... | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 93 | 1 | 12 |
| 15 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 90 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 59 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
16 rows × 24 columns
'Initial antibody cell/cluster table:'
cluster.ids
3 2280
1 1367
10 1018
9 488
2 351
14 348
4 242
5 224
24 194
26 64
22 43
12 41
Name: count, dtype: int64
'PBMC4 - contingency_matrix (rows: antibody - cols: monocle)'
| 1 | 2 | 3 | |
|---|---|---|---|
| 1 | 1342 | 5 | 0 |
| 2 | 335 | 5 | 0 |
| 3 | 8 | 2158 | 0 |
| 4 | 241 | 0 | 0 |
| 5 | 16 | 195 | 1 |
| 9 | 480 | 1 | 0 |
| 10 | 13 | 39 | 931 |
| 12 | 38 | 0 | 0 |
| 14 | 343 | 2 | 0 |
| 22 | 42 | 0 | 0 |
| 24 | 193 | 0 | 0 |
| 26 | 63 | 0 | 0 |
'PBMC4 - contingency_matrix (rows: antibody - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 744 | 0 | 0 | 0 | 195 | 247 | 0 | 8 | 1 | 0 | ... | 139 | 6 | 0 | 0 | 3 | 1 | 2 | 0 | 1 | 0 |
| 2 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 317 | 0 | ... | 2 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 0 |
| 3 | 1 | 0 | 573 | 514 | 1 | 0 | 405 | 0 | 0 | 273 | ... | 2 | 0 | 0 | 178 | 26 | 119 | 0 | 42 | 3 | 0 |
| 4 | 3 | 0 | 0 | 0 | 3 | 2 | 0 | 1 | 2 | 0 | ... | 8 | 30 | 174 | 0 | 0 | 0 | 18 | 0 | 0 | 0 |
| 5 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 14 | 0 | ... | 1 | 0 | 0 | 0 | 3 | 11 | 2 | 1 | 0 | 0 |
| 9 | 12 | 0 | 0 | 0 | 1 | 3 | 0 | 288 | 0 | 0 | ... | 2 | 130 | 8 | 0 | 1 | 0 | 36 | 0 | 0 | 0 |
| 10 | 1 | 657 | 1 | 0 | 0 | 0 | 1 | 0 | 6 | 1 | ... | 0 | 0 | 0 | 0 | 40 | 0 | 0 | 3 | 0 | 37 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | ... | 0 | 2 | 0 | 0 | 0 | 0 | 31 | 0 | 0 | 0 |
| 14 | 10 | 0 | 0 | 0 | 205 | 105 | 0 | 0 | 0 | 0 | ... | 23 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |
| 22 | 6 | 0 | 0 | 0 | 2 | 2 | 0 | 2 | 0 | 0 | ... | 0 | 24 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 9 | 0 | 0 | 0 | 97 | 75 | 0 | 0 | 1 | 0 | ... | 9 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 26 | 27 | 0 | 0 | 0 | 1 | 12 | 0 | 1 | 0 | 0 | ... | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
12 rows × 22 columns
'PBMC4 - contingency_matrix (rows: antibody - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 454 | 839 | 0 | 1 | 1 | 8 | 1 | 12 | 0 | 0 | 2 | 1 | 0 | 1 | 27 |
| 2 | 0 | 1 | 4 | 1 | 0 | 0 | 0 | 328 | 0 | 0 | 0 | 3 | 1 | 0 | 2 | 0 |
| 3 | 0 | 2 | 0 | 719 | 730 | 476 | 1 | 0 | 2 | 15 | 168 | 14 | 1 | 35 | 3 | 0 |
| 4 | 0 | 4 | 2 | 0 | 0 | 0 | 230 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 10 | 1 | 7 | 0 | 19 | 0 | 170 | 0 | 3 | 0 | 1 | 1 | 0 |
| 9 | 0 | 2 | 8 | 0 | 0 | 0 | 175 | 0 | 288 | 0 | 0 | 1 | 7 | 0 | 0 | 0 |
| 10 | 921 | 0 | 3 | 36 | 4 | 1 | 0 | 5 | 0 | 2 | 8 | 0 | 1 | 2 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 324 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 |
| 22 | 0 | 3 | 4 | 0 | 0 | 0 | 27 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 0 | 185 | 4 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 26 | 1 | 4 | 58 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC4 - contingency_matrix (rows: antibody - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 853 | 323 | 0 | 0 | 0 | 0 | 9 | 1 | 0 | 5 | 151 | 0 | 0 | 1 | 3 | 0 | 1 | 0 | 0 |
| 2 | 9 | 0 | 0 | 0 | 0 | 0 | 2 | 324 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 1 | 0 |
| 3 | 4 | 1 | 750 | 693 | 0 | 519 | 1 | 0 | 0 | 0 | 1 | 25 | 0 | 113 | 26 | 30 | 3 | 0 | 0 |
| 4 | 1 | 11 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 46 | 2 | 0 | 178 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 1 | 0 | 1 | 2 | 0 | 2 | 1 | 15 | 0 | 0 | 0 | 177 | 0 | 9 | 3 | 1 | 0 | 0 | 0 |
| 9 | 28 | 5 | 0 | 0 | 0 | 0 | 292 | 0 | 0 | 140 | 9 | 0 | 6 | 0 | 1 | 0 | 0 | 0 | 0 |
| 10 | 1 | 0 | 3 | 3 | 598 | 0 | 0 | 4 | 304 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 30 | 37 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 36 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 10 | 326 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
| 22 | 12 | 11 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 9 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 3 | 186 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 26 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 60 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC4 - contingency_matrix (rows: antibody - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 3 | 0 | 9 | 75 | 431 | 824 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | ... | 0 | 0 | 57 | 29 | 102 | 142 | 1 | 0 | 0 | 4 |
| 3 | 0 | 110 | 687 | 195 | 395 | 719 | 26 | 25 | 0 | 0 | ... | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 19 | 0 | 1 | 1 | 191 | 28 |
| 5 | 0 | 9 | 2 | 0 | 1 | 2 | 178 | 3 | 0 | 0 | ... | 0 | 0 | 0 | 4 | 10 | 1 | 0 | 0 | 0 | 1 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 41 | 0 | 292 | 6 | 12 | 129 |
| 10 | 33 | 1 | 4 | 1 | 2 | 2 | 1 | 0 | 24 | 25 | ... | 0 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 1 | 1 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 36 | 0 | 0 | 1 | 0 | 1 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 333 | 9 |
| 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 17 | 19 |
| 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 187 | 4 |
| 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 1 | 5 |
12 rows × 24 columns
Against cellTypist cluster number
print_clustering_data(tuning = 'celltypist',dataset="PBMC1")'Initial COTAN cluster number:'
14
'Initial monocle cluster number:'
14
'Initial scanpy cluster number:'
18
'Initial scvi-tools cluster number:'
17
'Initial seurat cluster number:'
20
'PBMC1 - contingency_matrix (rows: cellTypist - cols: monocle)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 1 | 3 | 1 | 273 | 0 | 0 | 0 | 237 | 227 | 147 | 0 | 6 | 6 | 0 | 77 | 1 | 0 |
| 2 | 66 | 0 | 0 | 0 | 0 | 230 | 228 | 218 | 0 | 0 | 0 | 145 | 25 | 0 | 31 | 0 | 0 | 0 |
| 3 | 3 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 36 | 0 | 6 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 73 | 0 |
| 5 | 200 | 0 | 0 | 7 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 65 | 0 | 35 | 0 | 0 | 0 |
| 6 | 0 | 142 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 36 | 0 | 0 | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 1 | 267 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 9 | 2 | 0 | 0 | 0 | 0 | 22 | 15 | 20 | 0 | 0 | 0 | 21 | 0 | 0 | 1 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 37 | 0 | 0 | 131 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 21 | 49 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 27 | 0 | 0 | 179 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 4 | 14 | 0 | 10 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
| 14 | 0 | 155 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 |
'PBMC1 - contingency_matrix (rows: cellTypist - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 369 | 243 | 292 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 74 | 0 |
| 2 | 474 | 89 | 0 | 0 | 0 | 258 | 0 | 2 | 0 | 0 | 0 | 111 | 0 | 8 | 0 | 1 | 0 |
| 3 | 0 | 45 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 76 | 0 | 0 |
| 5 | 2 | 260 | 0 | 0 | 0 | 2 | 0 | 42 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 138 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 55 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 0 | 0 | 0 | 0 | 263 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 5 | 2 | 0 | 0 | 0 | 5 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 66 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 78 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 91 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 65 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 11 | 0 | 0 | 0 | 6 | 0 | 143 | 78 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 154 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: cellTypist - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 353 | 261 | 1 | 2 | 0 | 1 | 183 | 0 | 4 | 0 | 152 | 0 | 1 | 19 | 1 | 0 | 0 | 0 | 0 |
| 2 | 43 | 0 | 0 | 278 | 0 | 227 | 187 | 0 | 180 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 16 | 0 | 3 |
| 3 | 4 | 0 | 0 | 9 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 0 | 0 |
| 4 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 73 | 0 | 0 | 0 | 0 | 0 |
| 5 | 279 | 0 | 0 | 11 | 4 | 9 | 2 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 142 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 55 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 152 | 1 | 0 | 0 | 125 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 1 | 0 | 0 | 1 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 72 | 0 | 0 | 2 |
| 10 | 0 | 0 | 86 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 81 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 65 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 45 | 0 | 0 | 1 | 173 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 17 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 145 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 15 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: cellTypist - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 351 | 267 | 0 | 229 | 0 | 0 | 0 | 0 | ... | 0 | 3 | 0 | 1 | 88 | 0 | 0 | 40 | 0 | 0 |
| 2 | 423 | 119 | 0 | 0 | 238 | 0 | 154 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 1 | 0 |
| 3 | 1 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 0 |
| 4 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 74 | 0 | 0 | 0 | 0 |
| 5 | 0 | 258 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 142 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 0 | ... | 0 | 0 | 12 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 151 | 0 | 0 | ... | 0 | 0 | 5 | 123 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 10 | 2 | 0 | 0 | 11 | 0 | 11 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 46 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 38 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 130 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | ... | 0 | 0 | 66 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 1 | 3 | 0 | 0 | 4 | 0 | 0 | 0 | 138 | 0 | ... | 0 | 0 | 44 | 0 | 0 | 0 | 2 | 0 | 1 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 27 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 153 | ... | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 |
15 rows × 21 columns
'PBMC1 - contingency_matrix (rows: cellTypist - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 35 | 0 | 11 | 648 | 284 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 807 | 129 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37 | 2 | 8 |
| 4 | 0 | 73 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 308 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 142 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 1 | 0 | 0 | 56 |
| 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 55 | 4 | 220 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 34 | 1 | 0 | 0 | 45 | 1 |
| 10 | 0 | 1 | 146 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 67 | 1 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 175 | 0 | 1 | 5 | 56 |
| 13 | 0 | 1 | 0 | 0 | 0 | 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 152 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 |
print_clustering_data(tuning = 'celltypist',dataset="PBMC2")'Initial COTAN cluster number:'
17
'Initial monocle cluster number:'
17
'Initial scanpy cluster number:'
18
'Initial scvi-tools cluster number:'
20
'Initial seurat cluster number:'
19
'PBMC2 - contingency_matrix (rows: cellTypist - cols: monocle)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 0 | 0 | 1 | 0 | 5 | 0 | 55 | 0 | 0 | 12 | 38 | 6 | 90 | 1 | 0 | 0 | 22 |
| 2 | 0 | 0 | 0 | 0 | 0 | 279 | 0 | 6 | 22 | 0 | 53 | 22 | 24 | 1 | 19 | 0 | 0 | 1 |
| 3 | 0 | 1 | 332 | 1 | 407 | 130 | 338 | 27 | 297 | 36 | 202 | 95 | 99 | 38 | 132 | 0 | 0 | 7 |
| 4 | 577 | 1 | 0 | 6 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 2 | 42 | 0 | 0 | 0 | 75 |
| 5 | 1 | 0 | 0 | 0 | 0 | 15 | 0 | 258 | 0 | 0 | 9 | 9 | 2 | 13 | 0 | 0 | 0 | 9 |
| 6 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 77 | 0 |
| 7 | 0 | 566 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 142 | 0 | 32 | 4 | 69 | 4 | 11 | 292 | 24 | 59 | 4 | 2 | 31 | 0 | 0 | 0 |
| 9 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 173 | 0 | 0 |
| 10 | 43 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 3 |
| 11 | 0 | 0 | 0 | 228 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 204 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 3 | 0 | 0 | 0 | 66 | 3 | 3 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 |
'PBMC2 - contingency_matrix (rows: cellTypist - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 15 | 2 | 57 | 5 | 0 | 148 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 131 | 200 | 35 | 2 | 2 | 0 | 1 | 55 | 0 | 1 | 0 | 0 | 0 | 0 |
| 3 | 21 | 575 | 490 | 0 | 0 | 0 | 230 | 107 | 248 | 286 | 10 | 2 | 48 | 98 | 0 | 26 | 0 | 0 | 0 | 1 |
| 4 | 0 | 0 | 0 | 0 | 471 | 8 | 0 | 1 | 0 | 0 | 2 | 222 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 253 | 0 | 3 | 52 | 0 | 6 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88 | 0 | 0 |
| 7 | 0 | 0 | 0 | 464 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 0 | 88 | 0 | 0 | 0 |
| 8 | 558 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 112 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 174 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 228 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 204 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 44 | 17 | 1 | 1 | 1 | 2 | 3 | 0 | 1 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC2 - contingency_matrix (rows: cellTypist - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 1 | 0 | 217 | 0 | 0 | 0 | 7 | 0 | 3 | 0 | 3 | 0 | 0 | 0 | 0 |
| 2 | 348 | 2 | 0 | 0 | 0 | 4 | 7 | 0 | 2 | 3 | 60 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 469 | 4 | 1 | 15 | 1 | 412 | 69 | 356 | 324 | 322 | 165 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1 | 667 | 1 | 0 | 8 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 23 | 0 | 0 | 0 |
| 5 | 10 | 2 | 0 | 0 | 0 | 0 | 7 | 0 | 1 | 0 | 65 | 0 | 159 | 0 | 72 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 80 | 0 | 0 | 4 | 0 | 0 |
| 7 | 0 | 0 | 556 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| 8 | 1 | 0 | 1 | 561 | 0 | 2 | 99 | 1 | 2 | 2 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 173 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 47 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 228 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 204 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 0 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 |
| 15 | 61 | 0 | 0 | 0 | 0 | 3 | 2 | 1 | 1 | 3 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 |
'PBMC2 - contingency_matrix (rows: cellTypist - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 1 | 0 | 1 | 41 | 178 | 0 | 0 | 1 | 0 | 0 | 0 |
| 2 | 389 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 10 | 3 | 0 | 23 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 259 | 0 | 578 | 568 | 15 | 0 | 1 | 290 | 188 | 0 | 2 | 169 | 62 | 8 | 0 | 0 | 2 | 0 | 0 | 0 |
| 4 | 0 | 635 | 0 | 0 | 0 | 0 | 7 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 59 | 0 | 0 | 0 |
| 5 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 268 | 0 | 32 | 1 | 6 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 82 | 0 | 0 |
| 7 | 0 | 0 | 0 | 0 | 0 | 305 | 0 | 0 | 0 | 0 | 262 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 1 | 0 | 8 | 0 | 541 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 119 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 172 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 83 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 145 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 201 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 |
| 15 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 76 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
'PBMC2 - contingency_matrix (rows: cellTypist - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 3 | 4 | 220 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 416 | 9 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 2 | 1186 | 847 | 31 | 72 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 4 | 639 | 56 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 1 |
| 5 | 0 | 0 | 0 | 39 | 0 | 7 | 270 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 86 | 0 | 7 | 0 | 0 | 0 | 0 | 0 |
| 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88 | 300 | 174 | 0 | 0 | 3 | 1 |
| 8 | 0 | 0 | 568 | 9 | 1 | 96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 154 | 24 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 |
| 10 | 1 | 51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 145 | 69 | 14 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 22 | 180 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 0 | 0 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 1 |
| 15 | 0 | 1 | 0 | 6 | 72 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 |
print_clustering_data(tuning = 'celltypist',dataset="PBMC3")'Initial COTAN cluster number:'
23
'Initial monocle cluster number:'
23
'Initial scanpy cluster number:'
17
'Initial scvi-tools cluster number:'
18
'Initial seurat cluster number:'
20
'PBMC3 - contingency_matrix (rows: cellTypist - cols: monocle)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 860 | 0 | 0 | 0 | 38 | 559 | 313 | 338 | 231 | 324 | 158 | 0 | 195 | 0 | 0 | 5 | 0 |
| 2 | 0 | 1041 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 390 | 0 | 2 | 7 | 0 | 31 |
| 3 | 0 | 0 | 0 | 654 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 |
| 4 | 400 | 0 | 0 | 0 | 12 | 309 | 29 | 47 | 12 | 157 | 78 | 0 | 56 | 0 | 0 | 0 | 0 |
| 5 | 0 | 22 | 1036 | 33 | 22 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 2 | 121 | 0 |
| 6 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 144 | 0 | 0 |
| 7 | 0 | 0 | 0 | 0 | 134 | 1 | 247 | 305 | 248 | 0 | 135 | 1 | 42 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 3 | 0 | 435 | 0 | 3 | 7 | 0 | 0 | 12 | 0 | 20 | 0 | 0 | 4 | 0 |
| 9 | 0 | 0 | 0 | 396 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 73 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 328 | 2 | 0 | 1 |
| 11 | 0 | 0 | 8 | 0 | 277 | 2 | 21 | 26 | 1 | 0 | 47 | 0 | 46 | 0 | 0 | 2 | 0 |
| 12 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 13 | 11 | 0 | 0 | 0 | 0 | 8 | 157 | 9 | 19 | 1 | 7 | 0 | 21 | 0 | 0 | 0 | 0 |
| 14 | 0 | 0 | 79 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 19 | 0 |
| 15 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 |
| 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 |
| 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 0 | 0 |
| 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 |
'PBMC3 - contingency_matrix (rows: cellTypist - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1670 | 0 | 0 | 212 | 0 | 32 | 234 | 621 | 23 | 228 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 1228 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 202 | 0 | 0 | 5 | 0 | 35 | 0 |
| 3 | 0 | 0 | 0 | 0 | 543 | 0 | 5 | 0 | 0 | 1 | 0 | 0 | 0 | 111 | 0 | 0 | 2 | 0 |
| 4 | 29 | 0 | 0 | 0 | 0 | 811 | 192 | 5 | 4 | 59 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 0 | 0 | 1028 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 29 | 145 | 33 | 0 | 0 | 0 | 0 |
| 6 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 149 | 0 | 0 | 0 |
| 7 | 1 | 0 | 0 | 702 | 0 | 0 | 230 | 99 | 7 | 73 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 3 | 0 | 0 | 0 | 47 | 0 | 433 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 395 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 10 | 0 | 94 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 311 | 1 | 0 | 0 | 1 | 0 | 1 | 0 |
| 11 | 0 | 0 | 2 | 0 | 0 | 0 | 116 | 0 | 290 | 21 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 |
| 13 | 16 | 0 | 0 | 44 | 0 | 0 | 10 | 55 | 1 | 107 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 0 | 11 | 0 | 0 | 0 | 4 | 0 | 5 | 1 | 0 | 0 | 90 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 |
| 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 0 | 0 |
| 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 |
'PBMC3 - contingency_matrix (rows: cellTypist - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 657 | 1415 | 2 | 2 | 43 | 614 | 91 | 5 | 0 | 64 | 0 | 14 | 49 | 0 | 0 | 32 | 31 | 0 | 0 |
| 2 | 1463 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 4 | 0 | 1 | 0 | 654 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 6 | 13 | 1 | 0 | 856 | 4 | 168 | 0 | 0 | 0 | 0 | 0 | 52 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 29 | 0 | 0 | 1172 | 32 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 1 | 0 |
| 6 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 147 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 1 | 827 | 5 | 0 | 0 | 1 | 187 | 35 | 22 | 0 | 0 | 0 | 24 | 8 | 0 | 0 | 1 | 2 | 0 | 0 |
| 8 | 0 | 30 | 0 | 2 | 0 | 1 | 3 | 12 | 305 | 0 | 131 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 396 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10 | 83 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 324 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 0 | 15 | 0 | 2 | 1 | 3 | 1 | 399 | 3 | 0 | 5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
| 13 | 0 | 49 | 21 | 0 | 0 | 0 | 22 | 6 | 0 | 0 | 0 | 0 | 131 | 2 | 0 | 0 | 2 | 0 | 0 | 0 |
| 14 | 0 | 0 | 0 | 31 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 80 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 |
| 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 52 | 0 | 0 | 0 | 0 |
| 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
'PBMC3 - contingency_matrix (rows: cellTypist - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1744 | 412 | 0 | 0 | 681 | 20 | 161 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 1011 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 227 | 0 | 231 | 3 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 535 | 0 | 2 | 0 | 1 | 2 | 0 | 0 | 120 | 0 | 0 |
| 4 | 16 | 6 | 0 | 0 | 3 | 886 | 188 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 5 | 0 | 1 | 4 | 1043 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 24 | 136 | 0 | 0 | 30 | 0 | 0 |
| 6 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 149 | 0 | 0 | 0 |
| 7 | 0 | 995 | 0 | 0 | 93 | 0 | 18 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 18 | 0 | 3 | 0 | 0 | 8 | 0 | 454 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 0 | 336 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 |
| 10 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 314 | 1 | 0 | 4 | 0 | 0 | 0 | 0 |
| 11 | 0 | 14 | 0 | 0 | 0 | 2 | 411 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 13 | 13 | 8 | 0 | 0 | 205 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 109 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
| 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 |
| 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 52 | 0 |
| 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
'PBMC3 - contingency_matrix (rows: cellTypist - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1822 | 80 | 633 | 127 | 291 | 53 | 12 | 0 | 0 | 3 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | ... | 68 | 517 | 404 | 324 | 0 | 0 | 0 | 1 | 0 | 0 |
| 3 | 1 | 0 | 2 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 9 | 136 | 387 | 108 | 15 | 0 |
| 4 | 482 | 430 | 156 | 9 | 5 | 17 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 1 | 0 | 138 | 2 | 28 | 281 | 755 | ... | 0 | 1 | 0 | 0 | 0 | 25 | 5 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 5 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 87 | 955 | 53 | 11 | 7 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 1 | 16 | 1 | 8 | 454 | 0 | 3 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 359 | 2 | 34 | 0 | 1 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 82 | 31 | 91 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 5 | 1 | 86 | 4 | 11 | 29 | 255 | 0 | 24 | 15 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 5 | 3 | 0 | 0 | 0 |
| 13 | 7 | 0 | 210 | 5 | 1 | 10 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 0 | 0 | 1 | 0 | 10 | 0 | 0 | 100 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 52 |
| 19 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 8 | 0 |
19 rows × 23 columns
print_clustering_data(tuning = 'celltypist',dataset="PBMC4")'Initial COTAN cluster number:'
15
'Initial monocle cluster number:'
21
'Initial scanpy cluster number:'
16
'Initial scvi-tools cluster number:'
18
'Initial seurat cluster number:'
18
'PBMC4 - contingency_matrix (rows: cellTypist - cols: monocle)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 12 | 0 | 385 | 1 | 0 | 0 | 0 | 0 | 0 |
| 2 | 756 | 2 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 41 |
| 3 | 0 | 707 | 149 | 0 | 0 | 0 | 379 | 0 | 3 | 0 | 93 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 98 | 0 | 8 | 1 | 0 | 0 | 0 | 0 | 0 |
| 5 | 5 | 2 | 2 | 648 | 642 | 406 | 0 | 1 | 422 | 3 | 2 | 3 | 35 | 0 | 29 | 0 |
| 6 | 0 | 196 | 83 | 0 | 0 | 0 | 19 | 1 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 51 | 0 | 0 | 0 | 0 | 9 | 0 | 15 | 2 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 2 | 417 | 0 | 0 | 0 | 77 | 0 | 0 | 5 | 37 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 351 | 1 | 3 | 4 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 2 | 3 | 76 | 0 | 0 | 5 | 0 | 0 | 219 | 2 | 0 | 0 | 0 |
| 11 | 220 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 2 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 0 |
| 13 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 4 | 0 | 0 |
| 14 | 0 | 4 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 97 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 11 | 5 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 72 | 3 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 0 | 0 |
'PBMC4 - contingency_matrix (rows: cellTypist - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 10 | 0 | 0 | 0 | 0 | 385 | 5 | 1 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 673 | 0 | 0 | 0 | 0 | 1 | 134 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 346 | 0 | 0 | 509 | 387 | 1 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 78 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 7 | 10 | 0 | 0 | 0 | 0 | 0 | 85 | 0 | 0 | 0 |
| 5 | 928 | 1 | 822 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 181 | 78 | 142 | 0 | 0 | 47 | 0 |
| 6 | 0 | 7 | 0 | 0 | 5 | 2 | 0 | 286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 |
| 7 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 67 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 |
| 8 | 0 | 483 | 0 | 0 | 8 | 43 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 9 | 0 | 1 | 0 | 0 | 0 | 0 | 5 | 0 | 348 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 |
| 10 | 74 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 216 | 10 | 2 | 2 | 0 | 0 | 1 | 0 |
| 11 | 0 | 0 | 0 | 46 | 0 | 0 | 0 | 0 | 0 | 177 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 0 |
| 13 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 68 | 0 | 0 | 1 | 22 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 89 | 2 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 |
'PBMC4 - contingency_matrix (rows: cellTypist - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 313 | 0 | 0 | 1 | 0 | 0 | 88 | 2 | 0 | 0 |
| 2 | 1 | 1 | 0 | 0 | 687 | 0 | 1 | 1 | 10 | 0 | 104 | 2 | 0 | 0 | 0 | 1 | 0 | 0 |
| 3 | 439 | 840 | 1 | 0 | 0 | 0 | 0 | 21 | 0 | 0 | 0 | 0 | 30 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 1 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 1 | 1 | 776 | 699 | 2 | 508 | 15 | 0 | 0 | 65 | 2 | 1 | 0 | 24 | 2 | 84 | 0 | 20 |
| 6 | 10 | 8 | 0 | 0 | 0 | 0 | 0 | 287 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 7 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 33 | 5 | 0 | 0 | 34 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 507 | 15 | 0 | 0 | 0 | 0 | 1 | 3 | 2 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 358 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 13 | 1 | 0 | 27 | 0 | 0 | 0 | 265 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 219 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
| 13 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 16 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 81 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 57 | 0 |
'PBMC4 - contingency_matrix (rows: cellTypist - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 5 | 0 | 0 | 0 | 0 | 0 | 398 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 632 | 0 | 1 | 0 | 122 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 52 | 0 |
| 3 | 361 | 843 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 122 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 107 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 2 | 0 | 771 | 732 | 0 | 447 | 2 | 0 | 2 | 0 | 1 | 1 | 0 | 136 | 28 | 35 | 0 | 0 | 43 |
| 6 | 31 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 68 | 5 | 0 | 189 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 69 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 525 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 354 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 7 | 0 | 86 | 0 | 0 | 0 | 0 | 0 | 213 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 223 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 0 | 0 | 0 |
| 13 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 |
| 14 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 101 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 0 | 0 | 1 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 0 | 0 |
'PBMC4 - contingency_matrix (rows: cellTypist - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 402 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 47 | 344 | 414 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 367 | 963 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 105 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 131 | 724 | 531 | 741 | 67 | 1 | 1 | 0 | 0 | 2 | 0 | 1 | 1 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 99 | 209 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 0 | 0 | 0 | 0 | 70 | 0 | 3 | 4 | 0 | 0 | 0 | 0 | 0 |
| 8 | 0 | 0 | 0 | 0 | 0 | 0 | 530 | 7 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 356 | 1 | 0 | 0 | 0 | 0 | 0 |
| 10 | 1 | 6 | 83 | 2 | 0 | 215 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 199 | 1 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
| 13 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 8 | 0 | 1 | 1 | 0 | 0 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 105 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 1 | 1 | 0 | 91 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 0 |
Against antibody cluster number
print_clustering_data(tuning = 'antibody',dataset="PBMC1")'Initial COTAN cluster number:'
11
'Initial monocle cluster number:'
11
'Initial scanpy cluster number:'
9
'Initial scvi-tools cluster number:'
11
'Initial seurat cluster number:'
10
'Initial antibody cell/cluster table:'
cluster.ids
7 1338
8 876
3 748
4 341
9 331
5 211
1 202
6 131
2 62
10 51
12 16
Name: count, dtype: int64
'PBMC1 - contingency_matrix (rows: antibody - cols: monocle)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 94 | 0 | 17 | 50 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 4 | 0 | 0 | 3 | 0 | 38 | 1 | 0 |
| 3 | 35 | 0 | 500 | 65 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 1 | 0 | 262 | 0 | 0 | 0 | 0 | 0 |
| 5 | 2 | 0 | 29 | 127 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 58 | 26 |
| 7 | 2 | 731 | 0 | 4 | 0 | 275 | 95 | 17 | 1 |
| 8 | 776 | 0 | 30 | 6 | 1 | 0 | 0 | 0 | 1 |
| 9 | 0 | 0 | 0 | 1 | 294 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 44 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 22 | 0 | 7 | 60 | 0 | 0 | 0 | 0 | 0 | 0 | 72 |
| 2 | 0 | 0 | 0 | 0 | 5 | 0 | 2 | 1 | 0 | 38 | 0 |
| 3 | 67 | 0 | 408 | 125 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 1 | 0 | 13 | 0 | 249 | 0 | 0 | 0 | 0 | 0 |
| 5 | 2 | 0 | 10 | 146 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 82 | 1 | 0 |
| 7 | 2 | 702 | 1 | 0 | 348 | 0 | 0 | 0 | 20 | 52 | 0 |
| 8 | 780 | 0 | 21 | 6 | 0 | 1 | 1 | 0 | 1 | 0 | 4 |
| 9 | 0 | 0 | 0 | 1 | 0 | 1 | 156 | 137 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 37 | 0 | 7 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 28 | 0 | 0 | 22 | 0 | 1 | 36 | 0 | 74 | 0 |
| 2 | 0 | 3 | 42 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 3 | 123 | 0 | 0 | 434 | 0 | 0 | 43 | 0 | 0 | 0 |
| 4 | 0 | 1 | 0 | 0 | 0 | 259 | 3 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 17 | 0 | 0 | 138 | 0 | 3 | 0 |
| 6 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 56 | 3 | 26 |
| 7 | 1 | 644 | 451 | 0 | 0 | 1 | 0 | 27 | 0 | 1 |
| 8 | 786 | 0 | 0 | 19 | 1 | 2 | 3 | 0 | 2 | 1 |
| 9 | 0 | 0 | 0 | 0 | 294 | 1 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 1 | 0 | 9 | 34 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 93 | 14 | 0 | 0 | 0 | 54 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 1 | 4 | 0 | 0 | 1 | 1 | 39 | 0 | 0 |
| 3 | 22 | 536 | 0 | 0 | 0 | 42 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 1 | 0 | 261 | 1 | 0 | 0 | 0 | 0 | 0 |
| 5 | 1 | 18 | 0 | 0 | 0 | 139 | 0 | 0 | 0 | 0 | 0 |
| 6 | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 56 | 25 |
| 7 | 2 | 1 | 524 | 488 | 2 | 0 | 0 | 0 | 89 | 18 | 1 |
| 8 | 766 | 40 | 0 | 0 | 1 | 5 | 1 | 0 | 0 | 0 | 1 |
| 9 | 0 | 0 | 0 | 0 | 2 | 0 | 151 | 142 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 8 | 36 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 8 | 2 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 54 | 12 | 59 |
| 2 | 1 | 1 | 40 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 528 | 33 |
| 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 214 | 0 | 0 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 135 | 22 | 0 |
| 6 | 0 | 56 | 1 | 1 | 1 | 25 | 0 | 3 | 0 | 0 | 0 |
| 7 | 33 | 17 | 118 | 668 | 284 | 1 | 0 | 0 | 1 | 1 | 2 |
| 8 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 9 | 4 | 34 | 765 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 293 | 0 | 2 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 40 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 6 | 0 | 0 |
print_clustering_data(tuning = 'antibody',dataset="PBMC1")'Initial COTAN cluster number:'
11
'Initial monocle cluster number:'
11
'Initial scanpy cluster number:'
9
'Initial scvi-tools cluster number:'
11
'Initial seurat cluster number:'
10
'Initial antibody cell/cluster table:'
cluster.ids
7 1338
8 876
3 748
4 341
9 331
5 211
1 202
6 131
2 62
10 51
12 16
Name: count, dtype: int64
'PBMC1 - contingency_matrix (rows: antibody - cols: monocle)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 94 | 0 | 17 | 50 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 4 | 0 | 0 | 3 | 0 | 38 | 1 | 0 |
| 3 | 35 | 0 | 500 | 65 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 1 | 0 | 262 | 0 | 0 | 0 | 0 | 0 |
| 5 | 2 | 0 | 29 | 127 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 58 | 26 |
| 7 | 2 | 731 | 0 | 4 | 0 | 275 | 95 | 17 | 1 |
| 8 | 776 | 0 | 30 | 6 | 1 | 0 | 0 | 0 | 1 |
| 9 | 0 | 0 | 0 | 1 | 294 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 44 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 22 | 0 | 7 | 60 | 0 | 0 | 0 | 0 | 0 | 0 | 72 |
| 2 | 0 | 0 | 0 | 0 | 5 | 0 | 2 | 1 | 0 | 38 | 0 |
| 3 | 67 | 0 | 408 | 125 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 1 | 0 | 13 | 0 | 249 | 0 | 0 | 0 | 0 | 0 |
| 5 | 2 | 0 | 10 | 146 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 82 | 1 | 0 |
| 7 | 2 | 702 | 1 | 0 | 348 | 0 | 0 | 0 | 20 | 52 | 0 |
| 8 | 780 | 0 | 21 | 6 | 0 | 1 | 1 | 0 | 1 | 0 | 4 |
| 9 | 0 | 0 | 0 | 1 | 0 | 1 | 156 | 137 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 37 | 0 | 7 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 28 | 0 | 0 | 22 | 0 | 1 | 36 | 0 | 74 | 0 |
| 2 | 0 | 3 | 42 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 3 | 123 | 0 | 0 | 434 | 0 | 0 | 43 | 0 | 0 | 0 |
| 4 | 0 | 1 | 0 | 0 | 0 | 259 | 3 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 17 | 0 | 0 | 138 | 0 | 3 | 0 |
| 6 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 56 | 3 | 26 |
| 7 | 1 | 644 | 451 | 0 | 0 | 1 | 0 | 27 | 0 | 1 |
| 8 | 786 | 0 | 0 | 19 | 1 | 2 | 3 | 0 | 2 | 1 |
| 9 | 0 | 0 | 0 | 0 | 294 | 1 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 1 | 0 | 9 | 34 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 93 | 14 | 0 | 0 | 0 | 54 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 1 | 4 | 0 | 0 | 1 | 1 | 39 | 0 | 0 |
| 3 | 22 | 536 | 0 | 0 | 0 | 42 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 1 | 0 | 261 | 1 | 0 | 0 | 0 | 0 | 0 |
| 5 | 1 | 18 | 0 | 0 | 0 | 139 | 0 | 0 | 0 | 0 | 0 |
| 6 | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 56 | 25 |
| 7 | 2 | 1 | 524 | 488 | 2 | 0 | 0 | 0 | 89 | 18 | 1 |
| 8 | 766 | 40 | 0 | 0 | 1 | 5 | 1 | 0 | 0 | 0 | 1 |
| 9 | 0 | 0 | 0 | 0 | 2 | 0 | 151 | 142 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 8 | 36 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 8 | 2 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 54 | 12 | 59 |
| 2 | 1 | 1 | 40 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 528 | 33 |
| 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 214 | 0 | 0 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 135 | 22 | 0 |
| 6 | 0 | 56 | 1 | 1 | 1 | 25 | 0 | 3 | 0 | 0 | 0 |
| 7 | 33 | 17 | 118 | 668 | 284 | 1 | 0 | 0 | 1 | 1 | 2 |
| 8 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 9 | 4 | 34 | 765 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 293 | 0 | 2 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 40 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 6 | 0 | 0 |
print_clustering_data(tuning = 'antibody',dataset="PBMC1")'Initial COTAN cluster number:'
11
'Initial monocle cluster number:'
11
'Initial scanpy cluster number:'
9
'Initial scvi-tools cluster number:'
11
'Initial seurat cluster number:'
10
'Initial antibody cell/cluster table:'
cluster.ids
7 1338
8 876
3 748
4 341
9 331
5 211
1 202
6 131
2 62
10 51
12 16
Name: count, dtype: int64
'PBMC1 - contingency_matrix (rows: antibody - cols: monocle)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 94 | 0 | 17 | 50 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 4 | 0 | 0 | 3 | 0 | 38 | 1 | 0 |
| 3 | 35 | 0 | 500 | 65 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 1 | 0 | 262 | 0 | 0 | 0 | 0 | 0 |
| 5 | 2 | 0 | 29 | 127 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 58 | 26 |
| 7 | 2 | 731 | 0 | 4 | 0 | 275 | 95 | 17 | 1 |
| 8 | 776 | 0 | 30 | 6 | 1 | 0 | 0 | 0 | 1 |
| 9 | 0 | 0 | 0 | 1 | 294 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 44 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: scanpy)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 22 | 0 | 7 | 60 | 0 | 0 | 0 | 0 | 0 | 0 | 72 |
| 2 | 0 | 0 | 0 | 0 | 5 | 0 | 2 | 1 | 0 | 38 | 0 |
| 3 | 67 | 0 | 408 | 125 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 1 | 0 | 13 | 0 | 249 | 0 | 0 | 0 | 0 | 0 |
| 5 | 2 | 0 | 10 | 146 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 82 | 1 | 0 |
| 7 | 2 | 702 | 1 | 0 | 348 | 0 | 0 | 0 | 20 | 52 | 0 |
| 8 | 780 | 0 | 21 | 6 | 0 | 1 | 1 | 0 | 1 | 0 | 4 |
| 9 | 0 | 0 | 0 | 1 | 0 | 1 | 156 | 137 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 37 | 0 | 7 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: scvi-tools)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 28 | 0 | 0 | 22 | 0 | 1 | 36 | 0 | 74 | 0 |
| 2 | 0 | 3 | 42 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 3 | 123 | 0 | 0 | 434 | 0 | 0 | 43 | 0 | 0 | 0 |
| 4 | 0 | 1 | 0 | 0 | 0 | 259 | 3 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 17 | 0 | 0 | 138 | 0 | 3 | 0 |
| 6 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 56 | 3 | 26 |
| 7 | 1 | 644 | 451 | 0 | 0 | 1 | 0 | 27 | 0 | 1 |
| 8 | 786 | 0 | 0 | 19 | 1 | 2 | 3 | 0 | 2 | 1 |
| 9 | 0 | 0 | 0 | 0 | 294 | 1 | 0 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 1 | 0 | 9 | 34 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: seurat)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 93 | 14 | 0 | 0 | 0 | 54 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 1 | 4 | 0 | 0 | 1 | 1 | 39 | 0 | 0 |
| 3 | 22 | 536 | 0 | 0 | 0 | 42 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 1 | 0 | 261 | 1 | 0 | 0 | 0 | 0 | 0 |
| 5 | 1 | 18 | 0 | 0 | 0 | 139 | 0 | 0 | 0 | 0 | 0 |
| 6 | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 56 | 25 |
| 7 | 2 | 1 | 524 | 488 | 2 | 0 | 0 | 0 | 89 | 18 | 1 |
| 8 | 766 | 40 | 0 | 0 | 1 | 5 | 1 | 0 | 0 | 0 | 1 |
| 9 | 0 | 0 | 0 | 0 | 2 | 0 | 151 | 142 | 0 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 8 | 36 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 8 | 2 | 0 | 0 | 0 | 0 | 0 |
'PBMC1 - contingency_matrix (rows: antibody - cols: COTAN)'
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 54 | 12 | 59 |
| 2 | 1 | 1 | 40 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 528 | 33 |
| 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 214 | 0 | 0 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 135 | 22 | 0 |
| 6 | 0 | 56 | 1 | 1 | 1 | 25 | 0 | 3 | 0 | 0 | 0 |
| 7 | 33 | 17 | 118 | 668 | 284 | 1 | 0 | 0 | 1 | 1 | 2 |
| 8 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 9 | 4 | 34 | 765 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 293 | 0 | 2 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 40 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 6 | 0 | 0 |
Default parameters
print_scores(tuning = 'default',dataset="PBMC1")'PBMC1 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 3 | 18 | 13 | 11 | 14 |
'PBMC1 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 0.106025 | 0.062012 | 0.087622 | 0.168956 | 0.155843 |
'PBMC1 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 194.710746 | 159.568549 | 193.151278 | 235.185139 | 195.201252 |
'PBMC1 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 3.046493 | 2.534291 | 2.547137 | 1.695538 | 2.147008 |
'PBMC1 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 0.182454 | -0.006178 | 0.148873 | 0.235918 | 0.184544 |
'PBMC1 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 187.272904 | 160.02968 | 199.227613 | 213.845901 | 190.312532 |
'PBMC1 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 2.582264 | 2.79635 | 2.842419 | 1.973727 | 2.274669 |
'PBMC1 - default labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.578257 | 0.384609 | 0.410140 | 0.979930 | 0.602512 | 0.366057 | 0.991705 |
| scanpy | 0.721042 | 0.404607 | 0.824980 | 0.640363 | 0.508176 | 0.787276 | 0.328021 |
| scvi-tools | 0.776232 | 0.599664 | 0.809790 | 0.745344 | 0.666244 | 0.811382 | 0.547068 |
| seurat | 0.793630 | 0.649593 | 0.784165 | 0.803327 | 0.705921 | 0.747450 | 0.666699 |
| COTAN | 0.787289 | 0.670392 | 0.803876 | 0.771373 | 0.723485 | 0.796154 | 0.657448 |
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.622344 | 0.439255 | 0.458549 | 0.968180 | 0.645589 | 0.421345 | 0.989178 |
| scanpy | 0.662480 | 0.389320 | 0.814398 | 0.558329 | 0.511739 | 0.851844 | 0.307424 |
| scvi-tools | 0.718265 | 0.557951 | 0.800919 | 0.651075 | 0.643426 | 0.842101 | 0.491625 |
| seurat | 0.747924 | 0.647338 | 0.787235 | 0.712353 | 0.712527 | 0.810669 | 0.626267 |
| COTAN | 0.727398 | 0.637277 | 0.792854 | 0.671926 | 0.705427 | 0.834262 | 0.596488 |
print_scores(tuning = 'default',dataset="PBMC2")'PBMC2 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 2 | 18 | 20 | 14 | 17 |
'PBMC2 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 0.237524 | 0.077322 | 0.018324 | 0.134064 | 0.140061 |
'PBMC2 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 298.25227 | 270.502074 | 223.039427 | 367.295749 | 296.064689 |
'PBMC2 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 3.89379 | 2.581588 | 3.703433 | 1.958013 | 2.527846 |
'PBMC2 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 0.283181 | 0.1987 | 0.064022 | 0.358299 | 0.274624 |
'PBMC2 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 284.036162 | 259.900464 | 223.875031 | 377.870193 | 294.416967 |
'PBMC2 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 3.514194 | 2.322847 | 5.400931 | 1.992412 | 2.285385 |
'PBMC2 - default labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.393166 | 0.207180 | 0.245998 | 0.978626 | 0.521364 | 0.272813 | 0.996358 |
| scanpy | 0.718820 | 0.457213 | 0.804000 | 0.649960 | 0.556684 | 0.814847 | 0.380314 |
| scvi-tools | 0.699788 | 0.424696 | 0.785920 | 0.630670 | 0.525031 | 0.762849 | 0.361352 |
| seurat | 0.775988 | 0.562430 | 0.819560 | 0.736815 | 0.640108 | 0.816395 | 0.501888 |
| COTAN | 0.729355 | 0.472800 | 0.745550 | 0.713848 | 0.562480 | 0.591410 | 0.534966 |
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.261544 | 0.093122 | 0.154169 | 0.861687 | 0.457589 | 0.212802 | 0.983954 |
| scanpy | 0.685644 | 0.523244 | 0.773965 | 0.615415 | 0.607208 | 0.814732 | 0.452543 |
| scvi-tools | 0.654391 | 0.485789 | 0.750936 | 0.579842 | 0.573975 | 0.779957 | 0.422391 |
| seurat | 0.748480 | 0.679377 | 0.793713 | 0.708124 | 0.734394 | 0.850648 | 0.634028 |
| COTAN | 0.703575 | 0.633882 | 0.697485 | 0.709773 | 0.704954 | 0.646778 | 0.768362 |
print_scores(tuning = 'default',dataset="PBMC3")'PBMC3 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 3 | 22 | 17 | 18 | 32 |
'PBMC3 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 0.173831 | 0.017764 | 0.066172 | 0.12701 | 0.09951 |
'PBMC3 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 565.456442 | 389.223708 | 568.006153 | 568.200931 | 382.825491 |
'PBMC3 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 3.238128 | 3.245809 | 2.168128 | 2.441035 | 2.751106 |
'PBMC3 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 0.214085 | 0.065185 | 0.226855 | 0.282982 | 0.1924 |
'PBMC3 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 531.480656 | 382.798915 | 537.03678 | 586.377699 | 392.740241 |
'PBMC3 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 2.634444 | 3.87979 | 2.321242 | 2.318734 | 3.32168 |
'PBMC3 - default labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.500696 | 0.233560 | 0.338609 | 0.960446 | 0.500077 | 0.252454 | 0.990587 |
| scanpy | 0.685919 | 0.462762 | 0.763719 | 0.622505 | 0.541286 | 0.750873 | 0.390201 |
| scvi-tools | 0.738418 | 0.579677 | 0.757237 | 0.720511 | 0.635237 | 0.710070 | 0.568291 |
| seurat | 0.770512 | 0.585110 | 0.821173 | 0.725738 | 0.644073 | 0.789750 | 0.525267 |
| COTAN | 0.723833 | 0.527470 | 0.849029 | 0.630815 | 0.609217 | 0.882695 | 0.420469 |
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.469679 | 0.196545 | 0.316293 | 0.911906 | 0.473204 | 0.228265 | 0.980975 |
| scanpy | 0.675464 | 0.545251 | 0.756553 | 0.610075 | 0.612243 | 0.807482 | 0.464211 |
| scvi-tools | 0.721883 | 0.667226 | 0.738229 | 0.706244 | 0.710211 | 0.754868 | 0.668197 |
| seurat | 0.749240 | 0.667468 | 0.798924 | 0.705374 | 0.712865 | 0.824327 | 0.616475 |
| COTAN | 0.677769 | 0.535850 | 0.798894 | 0.588538 | 0.606241 | 0.820085 | 0.448159 |
print_scores(tuning = 'default',dataset="PBMC4")'PBMC4 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 3 | 22 | 16 | 19 | 24 |
'PBMC4 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 0.081399 | 0.063742 | 0.075337 | 0.12954 | 0.11378 |
'PBMC4 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 364.985136 | 267.681245 | 341.396665 | 364.393784 | 293.065775 |
'PBMC4 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 3.354088 | 2.496024 | 2.226 | 2.224448 | 2.33448 |
'PBMC4 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 0.193766 | 0.025023 | 0.077663 | 0.187532 | 0.030818 |
'PBMC4 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 353.871309 | 254.540593 | 284.048471 | 347.979408 | 255.247203 |
'PBMC4 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 2.775993 | 3.467425 | 2.808762 | 2.299231 | 3.246869 |
'PBMC4 - default labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.617025 | 0.470070 | 0.453383 | 0.965513 | 0.647279 | 0.425154 | 0.985455 |
| scanpy | 0.701228 | 0.380357 | 0.819943 | 0.612541 | 0.487560 | 0.777350 | 0.305802 |
| scvi-tools | 0.739299 | 0.504966 | 0.788229 | 0.696088 | 0.584900 | 0.745208 | 0.459077 |
| seurat | 0.760207 | 0.494746 | 0.847372 | 0.689301 | 0.583823 | 0.820228 | 0.415555 |
| COTAN | 0.716555 | 0.435917 | 0.808134 | 0.643618 | 0.526063 | 0.714778 | 0.387173 |
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.636102 | 0.525318 | 0.482349 | 0.933740 | 0.689338 | 0.484673 | 0.980428 |
| scanpy | 0.642165 | 0.368291 | 0.789153 | 0.541335 | 0.491476 | 0.822336 | 0.293735 |
| scvi-tools | 0.698167 | 0.482058 | 0.770861 | 0.638003 | 0.577373 | 0.767464 | 0.434365 |
| seurat | 0.691539 | 0.444871 | 0.803112 | 0.607186 | 0.550863 | 0.803654 | 0.377588 |
| COTAN | 0.625477 | 0.351843 | 0.733504 | 0.545185 | 0.461853 | 0.646787 | 0.329796 |
Matching cellTypist clusters number
print_scores(tuning = 'celltypist',dataset="PBMC1")'PBMC1 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 18 | 17 | 20 | 21 | 14 |
'PBMC1 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 0.022324 | 0.070608 | 0.079342 | 0.108506 | 0.155843 | 0.116981 |
'PBMC1 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 128.949194 | 160.591477 | 164.988143 | 187.121919 | 195.201252 | 193.803845 |
'PBMC1 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 2.806118 | 2.79721 | 2.797975 | 2.074196 | 2.147008 | 1.690698 |
'PBMC1 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | -0.045223 | 0.035629 | -0.025941 | 0.041531 | 0.184544 | 0.436382 |
'PBMC1 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 105.612108 | 166.682035 | 142.22761 | 162.068208 | 190.312532 | 254.936556 |
'PBMC1 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 5.698571 | 3.900272 | 4.09973 | 3.674422 | 2.274669 | 1.469953 |
'PBMC1 - matching celltypist labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.658065 | 0.341945 | 0.757164 | 0.581903 | 0.448601 | 0.714098 | 0.281814 |
| scanpy | 0.735830 | 0.459735 | 0.822412 | 0.665742 | 0.553086 | 0.794910 | 0.384828 |
| scvi-tools | 0.699950 | 0.375082 | 0.808964 | 0.616828 | 0.479652 | 0.748060 | 0.307550 |
| seurat | 0.730468 | 0.423069 | 0.849278 | 0.640820 | 0.527386 | 0.822925 | 0.337985 |
| COTAN | 0.787289 | 0.670392 | 0.803876 | 0.771373 | 0.723485 | 0.796154 | 0.657448 |
print_scores(tuning = 'celltypist',dataset="PBMC2")'PBMC2 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 18 | 20 | 19 | 20 | 17 |
'PBMC2 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | -0.018715 | 0.041013 | 0.045461 | 0.084324 | 0.140061 | 0.153673 |
'PBMC2 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 174.411397 | 246.631074 | 268.231569 | 319.598571 | 296.064689 | 402.453835 |
'PBMC2 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 3.376582 | 2.743121 | 3.804791 | 2.076454 | 2.527846 | 1.374222 |
'PBMC2 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 0.01541 | 0.107595 | 0.143462 | 0.181639 | 0.274624 | 0.430025 |
'PBMC2 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 166.030172 | 236.1466 | 264.309361 | 321.631607 | 294.416967 | 456.336419 |
'PBMC2 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 3.978911 | 3.444155 | 4.011805 | 2.517532 | 2.285385 | 1.14756 |
'PBMC2 - matching celltypist labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.605942 | 0.312112 | 0.699644 | 0.534375 | 0.425421 | 0.696157 | 0.259975 |
| scanpy | 0.697287 | 0.377675 | 0.809335 | 0.612491 | 0.492889 | 0.812324 | 0.299067 |
| scvi-tools | 0.709450 | 0.398779 | 0.791930 | 0.642531 | 0.500807 | 0.730881 | 0.343158 |
| seurat | 0.738307 | 0.418942 | 0.850535 | 0.652244 | 0.529176 | 0.837949 | 0.334181 |
| COTAN | 0.729355 | 0.472800 | 0.745550 | 0.713848 | 0.562480 | 0.591410 | 0.534966 |
print_scores(tuning = 'celltypist',dataset="PBMC3")'PBMC3 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 17 | 18 | 20 | 18 | 23 |
'PBMC3 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | -0.028636 | 0.05187 | 0.012342 | 0.127418 | 0.055352 | 0.143797 |
'PBMC3 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 294.452653 | 454.311295 | 471.87183 | 568.38436 | 398.465918 | 623.45755 |
'PBMC3 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 4.102941 | 3.008669 | 2.201609 | 2.442825 | 2.650649 | 1.411931 |
'PBMC3 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | -0.0339 | 0.256035 | 0.182746 | 0.282975 | 0.087495 | 0.386958 |
'PBMC3 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 262.303562 | 476.72854 | 558.156073 | 586.066802 | 354.838428 | 919.615311 |
'PBMC3 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 11.445609 | 2.595482 | 2.392255 | 2.302532 | 2.872672 | 1.269121 |
'PBMC3 - matching celltypist labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.593459 | 0.350206 | 0.643738 | 0.550465 | 0.432058 | 0.574653 | 0.324847 |
| scanpy | 0.712344 | 0.545918 | 0.758076 | 0.671816 | 0.609354 | 0.757298 | 0.490312 |
| scvi-tools | 0.735127 | 0.565025 | 0.767444 | 0.705423 | 0.623277 | 0.727251 | 0.534168 |
| seurat | 0.771047 | 0.586941 | 0.821567 | 0.726381 | 0.645653 | 0.790801 | 0.527147 |
| COTAN | 0.670200 | 0.459778 | 0.736074 | 0.615148 | 0.530378 | 0.652757 | 0.430943 |
print_scores(tuning = 'celltypist',dataset="PBMC4")'PBMC4 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 16 | 18 | 18 | 19 | 15 |
'PBMC4 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 0.037346 | 0.061345 | 0.125126 | 0.129018 | 0.103684 | 0.090862 |
'PBMC4 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 297.000318 | 297.049456 | 372.487294 | 364.118805 | 331.149713 | 364.544605 |
'PBMC4 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 2.555911 | 2.471675 | 1.925302 | 2.227526 | 2.857269 | 1.625159 |
'PBMC4 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 0.046459 | 0.09315 | 0.17079 | 0.186411 | 0.063028 | 0.425774 |
'PBMC4 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 249.579567 | 283.799441 | 376.317639 | 348.894242 | 304.688587 | 498.170533 |
'PBMC4 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | celltypist | |
|---|---|---|---|---|---|---|
| 0 | 3.292834 | 3.340857 | 2.072035 | 2.298001 | 2.481324 | 1.095999 |
'PBMC4 - matching celltypist labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.686019 | 0.421166 | 0.747399 | 0.633956 | 0.512728 | 0.700065 | 0.375523 |
| scanpy | 0.730100 | 0.473433 | 0.810168 | 0.664434 | 0.562407 | 0.778855 | 0.406111 |
| scvi-tools | 0.752718 | 0.500477 | 0.831022 | 0.687899 | 0.587099 | 0.808920 | 0.426105 |
| seurat | 0.759495 | 0.492528 | 0.846776 | 0.688525 | 0.581840 | 0.818184 | 0.413768 |
| COTAN | 0.724737 | 0.449720 | 0.766268 | 0.687477 | 0.534619 | 0.667192 | 0.428388 |
Matching antibody clusters number
print_scores(tuning = 'antibody',dataset="PBMC1")'PBMC1 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 9 | 11 | 10 | 11 | 11 |
'PBMC1 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 0.123097 | 0.097602 | 0.094258 | 0.171754 | 0.108557 | 0.069567 |
'PBMC1 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 203.253687 | 193.550388 | 189.978034 | 237.429051 | 166.755584 | 131.570445 |
'PBMC1 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 2.027098 | 1.886001 | 1.890236 | 1.677632 | 2.486428 | 2.515129 |
'PBMC1 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 0.101586 | 0.297043 | 0.261342 | 0.218776 | 0.106476 | 0.245087 |
'PBMC1 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 184.932102 | 202.80892 | 206.264825 | 214.61793 | 153.161104 | 131.570445 |
'PBMC1 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 2.395779 | 1.863548 | 1.700282 | 1.947515 | 2.755616 | 2.515129 |
'PBMC1 - matching antibody labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.724319 | 0.641765 | 0.727281 | 0.721381 | 0.707943 | 0.753325 | 0.665295 |
| scanpy | 0.746106 | 0.652841 | 0.792721 | 0.704669 | 0.717629 | 0.829289 | 0.621003 |
| scvi-tools | 0.757587 | 0.658079 | 0.782127 | 0.734540 | 0.721084 | 0.800236 | 0.649760 |
| seurat | 0.749425 | 0.642110 | 0.790860 | 0.712116 | 0.708375 | 0.813318 | 0.616972 |
| COTAN | 0.716421 | 0.637108 | 0.744518 | 0.690367 | 0.703698 | 0.787585 | 0.628745 |
print_scores(tuning = 'antibody',dataset="PBMC2")'PBMC2 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 11 | 10 | 12 | 12 | 12 |
'PBMC2 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | -0.032335 | 0.052435 | -0.009451 | 0.111841 | 0.085442 | 0.057103 |
'PBMC2 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 196.870204 | 272.65961 | 193.762495 | 291.540798 | 222.099829 | 197.474522 |
'PBMC2 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 3.377139 | 2.347558 | 4.634488 | 1.849581 | 2.78642 | 2.980073 |
'PBMC2 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 0.040432 | 0.266462 | 0.080701 | 0.359544 | 0.238423 | 0.23847 |
'PBMC2 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 195.298772 | 291.365112 | 211.716959 | 297.530428 | 239.08615 | 197.474522 |
'PBMC2 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 6.357385 | 2.018126 | 3.788174 | 1.490357 | 2.063593 | 2.980073 |
'PBMC2 - matching antibody labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.590346 | 0.458018 | 0.610332 | 0.571627 | 0.544156 | 0.612308 | 0.483589 |
| scanpy | 0.746517 | 0.649454 | 0.756633 | 0.736667 | 0.707302 | 0.779740 | 0.641594 |
| scvi-tools | 0.672359 | 0.576869 | 0.708154 | 0.640009 | 0.646073 | 0.750157 | 0.556430 |
| seurat | 0.759142 | 0.760752 | 0.776572 | 0.742477 | 0.800906 | 0.838459 | 0.765035 |
| COTAN | 0.735282 | 0.673070 | 0.687591 | 0.790082 | 0.743686 | 0.649622 | 0.851370 |
print_scores(tuning = 'antibody',dataset="PBMC3")'PBMC3 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 12 | 14 | 13 | 14 | 12 |
'PBMC3 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | -0.040176 | 0.034398 | 0.001717 | 0.076119 | 0.066886 | 0.037871 |
'PBMC3 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 303.562678 | 332.440157 | 368.948628 | 434.276887 | 338.97586 | 309.45087 |
'PBMC3 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 3.604809 | 3.434343 | 3.282334 | 2.612535 | 3.274719 | 3.04294 |
'PBMC3 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 0.074904 | 0.23307 | 0.17138 | 0.305558 | 0.220006 | 0.205664 |
'PBMC3 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 298.489075 | 382.997054 | 438.502899 | 489.196185 | 393.696152 | 309.45087 |
'PBMC3 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 12.995929 | 2.882473 | 4.362136 | 1.884472 | 2.454523 | 3.04294 |
'PBMC3 - matching antibody labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.644484 | 0.537094 | 0.639574 | 0.649469 | 0.598577 | 0.600917 | 0.596245 |
| scanpy | 0.729603 | 0.683244 | 0.752370 | 0.708173 | 0.724410 | 0.784386 | 0.669020 |
| scvi-tools | 0.726492 | 0.670063 | 0.728625 | 0.724372 | 0.713239 | 0.729596 | 0.697249 |
| seurat | 0.764843 | 0.698339 | 0.799673 | 0.732920 | 0.738860 | 0.829783 | 0.657901 |
| COTAN | 0.691237 | 0.607331 | 0.643954 | 0.746015 | 0.676860 | 0.575699 | 0.795798 |
print_scores(tuning = 'antibody',dataset="PBMC4")'PBMC4 - number of clusters'
| monocle | scanpy | scvi-tools | seurat | COTAN | |
|---|---|---|---|---|---|
| 0 | 13 | 11 | 11 | 13 | 12 |
'PBMC4 - Silhuette (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 0.003249 | 0.048981 | 0.047987 | 0.075298 | 0.044429 | -0.042083 |
'PBMC4 - Calinski_Harabasz (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 235.748317 | 272.355681 | 313.161524 | 323.350677 | 198.84997 | 196.596426 |
'PBMC4 - davies_bouldin (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 2.927763 | 2.81827 | 2.00597 | 2.506347 | 2.779589 | 4.520827 |
'PBMC4 - Silhuette from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 0.037 | 0.149413 | 0.138041 | 0.120031 | 0.068881 | 0.189162 |
'PBMC4 - Calinski_Harabasz from Prob. (higher is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 191.855254 | 247.966629 | 308.946333 | 300.355027 | 217.217336 | 196.596426 |
'PBMC4 - davies_bouldin from Prob. (lower is better)'
| monocle | scanpy | scvi-tools | seurat | COTAN | antibody | |
|---|---|---|---|---|---|---|
| 0 | 3.397778 | 2.505744 | 2.407189 | 2.295438 | 2.355103 | 4.520827 |
'PBMC4 - matching antibody labels'
| NMI | ARI | homogeneity | completeness | fowlkes_mallows | precision | recall | |
|---|---|---|---|---|---|---|---|
| monocle | 0.642736 | 0.462311 | 0.694650 | 0.598042 | 0.558509 | 0.724062 | 0.430809 |
| scanpy | 0.718317 | 0.585612 | 0.758534 | 0.682150 | 0.662578 | 0.798043 | 0.550107 |
| scvi-tools | 0.730060 | 0.588797 | 0.763135 | 0.699732 | 0.665395 | 0.803057 | 0.551331 |
| seurat | 0.722468 | 0.570140 | 0.785446 | 0.668840 | 0.651248 | 0.815130 | 0.520315 |
| COTAN | 0.675609 | 0.518293 | 0.678118 | 0.673118 | 0.606106 | 0.659437 | 0.557089 |
Check cellTypist vs Antibody
def compute_clustering_scores(output_dir, dataset):#celltypist_df, antibody_df,
# Merge the dataframes on the common 'cell' column
#cotan_df = pd.read_csv(f'{DIR}{dataset}/COTAN/antibody/clustering_labels.csv', index_col=0)
#display("Cotan clusters objetc dimension ",cotan_df.shape)
#display("----------------------------------------")
celltypist_df = pd.read_csv(f'{DIR}{dataset}/celltypist/celltypist_labels.csv', index_col=0)
celltypist_df.index = celltypist_df.index.str[:-2]
antibody_df = pd.read_csv(f'{DIR}{dataset}/antibody_annotation/antibody_labels_postproc.csv', index_col=0)
#antibody_df = labels_df.merge(antibody_df, how='inner', on='cell')
#all_in_antibody = celltypist_df.index.isin(antibody_df.index).all()
#all_in_celltypist = antibody_df.index.isin(celltypist_df.index).all()
#display("All celltypist indices in antibody: ",all_in_antibody, celltypist_df.index.isin(antibody_df.index).sum(),celltypist_df.shape)
#display("All antibody indices in cellTypist:", all_in_celltypist)
#display("----------------------------------------")
merged_df = celltypist_df.merge(antibody_df, how='inner',left_index=True, right_index=True)# on='cell')
merged_df.columns = ['cluster_celltypist','cluster_antibody']
# Initialize scores dictionary
scores = {
'NMI': normalized_mutual_info_score(merged_df['cluster_celltypist'], merged_df['cluster_antibody'], average_method='arithmetic'),
'ARI': adjusted_rand_score(merged_df['cluster_celltypist'], merged_df['cluster_antibody']),
'Homogeneity': homogeneity_score(merged_df['cluster_celltypist'], merged_df['cluster_antibody']),
'Completeness': completeness_score(merged_df['cluster_celltypist'], merged_df['cluster_antibody']),
'Fowlkes_Mallows': fowlkes_mallows_score(merged_df['cluster_celltypist'], merged_df['cluster_antibody'])
}
# Convert scores to DataFrame
scores_df = pd.DataFrame([scores])
# Save scores to CSV and LaTeX
#scores_df.to_csv(f'{output_dir}{dataset}/clustering_comparison_scores.csv')
#scores_df.to_latex(f'{output_dir}{dataset}/clustering_comparison_scores.tex')
# Display scores DataFrame
display(scores_df)
for dataset in DATASET_NAMES:
#display('------------------------------')
display(f'{dataset} - Clustering Comparison between CellTypist and Antibody')
# Assuming celltypist_df and antibody_df are defined elsewhere and available here
compute_clustering_scores(DIR, dataset)'PBMC1 - Clustering Comparison between CellTypist and Antibody'
| NMI | ARI | Homogeneity | Completeness | Fowlkes_Mallows | |
|---|---|---|---|---|---|
| 0 | 0.752326 | 0.731095 | 0.708308 | 0.802178 | 0.78126 |
'PBMC2 - Clustering Comparison between CellTypist and Antibody'
| NMI | ARI | Homogeneity | Completeness | Fowlkes_Mallows | |
|---|---|---|---|---|---|
| 0 | 0.659259 | 0.481537 | 0.667725 | 0.651004 | 0.585734 |
'PBMC3 - Clustering Comparison between CellTypist and Antibody'
| NMI | ARI | Homogeneity | Completeness | Fowlkes_Mallows | |
|---|---|---|---|---|---|
| 0 | 0.693433 | 0.555502 | 0.693429 | 0.693436 | 0.618105 |
'PBMC4 - Clustering Comparison between CellTypist and Antibody'
| NMI | ARI | Homogeneity | Completeness | Fowlkes_Mallows | |
|---|---|---|---|---|---|
| 0 | 0.751294 | 0.7252 | 0.728817 | 0.775201 | 0.776972 |
Summary
External measures
def load_scores(tuning, dataset):
scores = pd.read_csv(f'{DIR}{dataset}/scores_{tuning}.csv')
scores = scores.rename(columns={"Unnamed: 0": "tool"})
scores['tuning'] = tuning
return scoresdatasets = ['PBMC1', 'PBMC2', 'PBMC3', 'PBMC4']
tunings = ['default_celltypist', 'default_antibody', 'celltypist_celltypist', 'antibody_antibody']
scores_list = []
# Concatenate all scores into one DataFrame
for dataset in datasets:
for tuning in tunings:
scores = load_scores(tuning, dataset)
scores['dataset'] = dataset
scores_list.append(scores)
all_scores = pd.concat(scores_list)
# Prepare data for plotting
all_scores_melted = all_scores.melt(id_vars=['tool', 'tuning', 'dataset'], var_name='score', value_name='value')
sns.set_context("talk")
# Define custom colors
custom_palette = {
"seurat": "#4575B4",
"monocle": "#DAABE9",
"scanpy": "#7F9B5C",
"COTAN": "#F73604",
"scvi-tools": "#B6A18F"
}
g = sns.FacetGrid(all_scores_melted, row='score', col='tuning', sharey=False, height=4, aspect=1.3)
g.map(sns.pointplot, 'tool', 'value', palette=custom_palette,capsize=0.2, errwidth=2)
# Set titles and labels
g.set_titles(col_template="{col_name}", row_template="{row_name}")
g.set_axis_labels("Tool", "Score Value")
plt.subplots_adjust(top=1.4)
#g.fig.suptitle('Comparison of Clustering Tools by Various Scores and Conditions')
# Rotate x-axis labels
for ax in g.axes.flatten():
plt.setp(ax.get_xticklabels(), rotation=45)
g.savefig("ClusteringToolsComparison.pdf")
plt.show()
Internal measures
# Load your data (assuming you have CSV files for the scores)
def load_scores(tuning, dataset, score_type):
file_path = f'{DIR}{dataset}/{tuning}_{score_type}.csv'
print(f"Loading {file_path}")
scores = pd.read_csv(file_path, header=0) # Read the CSV file without an index column
scores_melted = scores.melt(var_name='tool', value_name='value')
scores_melted['tuning'] = tuning
scores_melted['dataset'] = dataset
scores_melted['score_type'] = score_type
return scores_melted
datasets = ['PBMC1', 'PBMC2', 'PBMC3', 'PBMC4']
tunings = ['default', 'celltypist', 'antibody']
score_types = ['silhouette', 'davies_bouldin','Calinski_Harabasz','silhouette_fromProb', 'davies_bouldin_fromProb','Calinski_Harabasz_fromProb']
scores_list = []
# Concatenate all scores into one DataFrame
for dataset in datasets:
for tuning in tunings:
for score_type in score_types:
scores = load_scores(tuning, dataset, score_type)
scores_list.append(scores)
all_scores = pd.concat(scores_list)
# Debug: Check the loaded data
print(all_scores.head())
# Define custom colors
custom_palette = {
"seurat": "#4575B4",
"monocle": "#DAABE9",
"scanpy": "#7F9B5C",
"COTAN": "#F73604",
"scvi-tools": "#B6A18F"
}
# Filter for silhouette and davies_bouldin scores
silhouette_scores = all_scores[all_scores['score_type'] == 'silhouette']
davies_bouldin_scores = all_scores[all_scores['score_type'] == 'davies_bouldin']
Calinski_Harabasz_scores = all_scores[all_scores['score_type'] == 'Calinski_Harabasz']
silhouette_scores_fromProb = all_scores[all_scores['score_type'] == 'silhouette_fromProb']
davies_bouldin_scores_fromProb = all_scores[all_scores['score_type'] == 'davies_bouldin_fromProb']
Calinski_Harabasz_scores_fromProb = all_scores[all_scores['score_type'] == 'Calinski_Harabasz_fromProb']
# Plot Silhouette scores
g1 = sns.FacetGrid(silhouette_scores, col='tuning', sharey=False, height=4, aspect=1.8)
g1.map(sns.pointplot, 'tool', 'value', palette=custom_palette, order=[ "monocle", "scanpy", "scvi-tools","seurat","COTAN"],capsize=0.2, errwidth=2)
g1.set_titles(col_template="{col_name}")
g1.set_axis_labels("Tool", "Silhouette Score")
g1.fig.suptitle('Silhouette Scores by Tool and Tuning Condition', y=1.25)
plt.subplots_adjust(top=0.85)
# Rotate x-axis labels
for ax in g1.axes.flatten():
plt.setp(ax.get_xticklabels(), rotation=45)
# Plot Davies-Bouldin scores
g2 = sns.FacetGrid(davies_bouldin_scores, col='tuning', sharey=False, height=4, aspect=1.8)
g2.map(sns.pointplot, 'tool', 'value', palette=custom_palette, order=["monocle", "scanpy", "scvi-tools","seurat","COTAN"],capsize=0.2, errwidth=2)
g2.set_titles(col_template="{col_name}")
g2.set_axis_labels("Tool", "Davies-Bouldin Score")
g2.fig.suptitle('Davies-Bouldin Scores by Tool and Tuning Condition', y=1.85)
plt.subplots_adjust(top=1.5)
# Rotate x-axis labels
for ax in g2.axes.flatten():
plt.setp(ax.get_xticklabels(), rotation=45)
# Plot Calinski_Harabasz scores
g3 = sns.FacetGrid(Calinski_Harabasz_scores, col='tuning', sharey=False, height=4, aspect=1.8)
g3.map(sns.pointplot, 'tool', 'value', palette=custom_palette, order=["monocle", "scanpy", "scvi-tools","seurat","COTAN"],capsize=0.2, errwidth=2)
g3.set_titles(col_template="{col_name}")
g3.set_axis_labels("Tool", "Calinski_Harabasz Score")
g3.fig.suptitle('Calinski Harabasz Scores by Tool and Tuning Condition', y=1.85)
plt.subplots_adjust(top=1.5)
# Rotate x-axis labels
for ax in g3.axes.flatten():
plt.setp(ax.get_xticklabels(), rotation=45)
# Plot Silhouette scores
g4 = sns.FacetGrid(silhouette_scores_fromProb, col='tuning', sharey=False, height=4, aspect=1.8)
g4.map(sns.pointplot, 'tool', 'value', palette=custom_palette, order=[ "monocle", "scanpy", "scvi-tools","seurat","COTAN"],capsize=0.2, errwidth=2)
g4.set_titles(col_template="{col_name}")
g4.set_axis_labels("Tool", "Silhouette Score")
g4.fig.suptitle('Silhouette Scores From Prob. by Tool and Tuning Condition', y=1.25)
plt.subplots_adjust(top=0.85)
# Rotate x-axis labels
for ax in g4.axes.flatten():
plt.setp(ax.get_xticklabels(), rotation=45)
# Plot Davies-Bouldin scores
g5 = sns.FacetGrid(davies_bouldin_scores_fromProb, col='tuning', sharey=False, height=4, aspect=1.8)
g5.map(sns.pointplot, 'tool', 'value', palette=custom_palette, order=["monocle", "scanpy", "scvi-tools","seurat","COTAN"],capsize=0.2, errwidth=2)
g5.set_titles(col_template="{col_name}")
g5.set_axis_labels("Tool", "Davies-Bouldin Score")
g5.fig.suptitle('Davies-Bouldin Scores From Prob. by Tool and Tuning Condition', y=1.85)
plt.subplots_adjust(top=1.5)
# Rotate x-axis labels
for ax in g5.axes.flatten():
plt.setp(ax.get_xticklabels(), rotation=45)
# Plot Calinski_Harabasz scores
g6 = sns.FacetGrid(Calinski_Harabasz_scores_fromProb, col='tuning', sharey=False, height=4, aspect=1.8)
g6.map(sns.pointplot, 'tool', 'value', palette=custom_palette, order=["monocle", "scanpy", "scvi-tools","seurat","COTAN"],capsize=0.2, errwidth=2)
g6.set_titles(col_template="{col_name}")
g6.set_axis_labels("Tool", "Calinski_Harabasz Score")
g6.fig.suptitle('Calinski Harabasz Scores From Prob. by Tool and Tuning Condition', y=1.85)
plt.subplots_adjust(top=1.5)
# Rotate x-axis labels
for ax in g6.axes.flatten():
plt.setp(ax.get_xticklabels(), rotation=45)
g1.savefig("Silhouette.pdf")
g2.savefig("Calinski_Harabasz.pdf")
g3.savefig("Davies_Bouldin.pdf")
g4.savefig("SilhouetteFromProb.pdf")
g5.savefig("Calinski_HarabaszFromProb.pdf")
g6.savefig("Davies_BouldinFromProb.pdf")
plt.show()Loading Data/PBMC1/default_silhouette.csv
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tool value tuning dataset score_type
0 Unnamed: 0 0.000000 default PBMC1 silhouette
1 monocle 0.106025 default PBMC1 silhouette
2 scanpy 0.062012 default PBMC1 silhouette
3 scvi-tools 0.087622 default PBMC1 silhouette
4 seurat 0.168956 default PBMC1 silhouette





